import pandas as pd
import gc
import numpy as np
import math
import scipy.stats as sts
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels as stats
from pandas.tseries.holiday import USFederalHolidayCalendar as calendar
import datetime
import re
import shap
from sklearn import preprocessing
import xgboost as xgb
import lightgbm as lgb
from xgboost import XGBClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import roc_auc_score
import catboost
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.metrics import roc_auc_score
import matplotlib.gridspec as gridspec
%matplotlib inline
# Standard plotly imports
import chart_studio.plotly as py
import plotly.graph_objs as go
import plotly.tools as tls
from plotly.offline import iplot, init_notebook_mode
import cufflinks
import cufflinks as cf
import plotly.figure_factory as ff
# Using plotly + cufflinks in offline mode
init_notebook_mode(connected=True)
cufflinks.go_offline(connected=True)
import warnings
warnings.filterwarnings("ignore")
import gc
gc.enable()
import logging
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
import os
def make_corr(variables, data, figsize=(10, 15)):
if isinstance(variables, pd.DataFrame):
variables = variables['Column Name'].tolist()
cols = variables
corr_matrix = data[cols].corr()
# Create a heatmap with the specified figsize
plt.figure(figsize=figsize)
sns.heatmap(corr_matrix, cmap='RdBu_r', annot=True, center=0.0)
plt.title('Correlation Heatmap for Columns Starting with C')
plt.show()
# We will focus on each column in detail
# Uniqe Values, DTYPE, NUNIQUE, NULL_RATE
def column_details(regex, df):
global columns
columns=[col for col in df.columns if re.search(regex, col)]
from colorama import Fore, Back, Style
print('Unique Values of the Features:\nfeature: DTYPE, NUNIQUE, NULL_RATE\n')
for i in df[columns]:
color = Fore.RED if df[i].dtype =='float64' else Fore.BLUE if df[i].dtype =='int64' else Fore.GREEN
print(f'{i}: {color} {df[i].dtype}, {df[i].nunique()}, %{round(df[i].isna().sum()/len(df[i])*100,2)}\n{Style.RESET_ALL}{pd.Series(df[i].unique()).sort_values().values}\n')
def null_values(df, rate=0):
"""a function to show null values with percentage"""
nv=pd.concat([df.isnull().sum(), 100 * df.isnull().sum()/df.shape[0]],axis=1).rename(columns={0:'Missing_Records', 1:'Percentage (%)'})
return nv[nv['Percentage (%)']>rate].sort_values('Percentage (%)', ascending=False)
#Plot Functions
def plot_col(col, df, figsize=(20, 6)):
"""
Function to create a pair of subplots containing two graphs based on a specified column.
Left Graph (First Subplot):
- Draws a bar graph representing the percentage of Fraud cases with respect to the specified column.
- Uses two colors (0 and 1) to represent Fraud and Non-Fraud cases.
- Adds a second line graph on the same column, representing the percentage of Fraud cases.
Right Graph (Second Subplot):
- Draws a bar graph representing the number of unique values in the dataset based on the specified column.
The purpose of this function is to visualize the relationship between Fraud status and the unique values in a specific column.
:param col: Name of the column to be visualized.
:param df: Dataset.
:param figsize: Size of the created figure.
"""
# Create a figure with two subplots
fig, ax = plt.subplots(1, 2, figsize=figsize, sharey=True)
# Left Graph: Bar graph and line graph for Fraud percentages
plt.subplot(121)
tmp = pd.crosstab(df[col], df['isFraud'], normalize='index') * 100
tmp = tmp.reset_index()
tmp.rename(columns={0: 'NoFraud', 1: 'Fraud'}, inplace=True)
ax[0] = sns.countplot(x=col, data=df, hue='isFraud',
order=np.sort(df[col].dropna().unique()))
ax[0].tick_params(axis='x', rotation=90)
ax_twin = ax[0].twinx()
ax_twin = sns.pointplot(x=col, y='Fraud', data=tmp, color='black', order=np.sort(df[col].dropna().unique()))
ax[0].grid()
# Right Graph: Bar graph for the number of unique values in the column
plt.subplot(122)
ax[1] = sns.countplot(x=df[col].dropna(),
order=np.sort(df[col].dropna().unique()))
plt.show()
#correlation functions
# Remove the highly collinear features from data
def remove_collinear_features(x, threshold):
'''
Objective:
Remove collinear features in a dataframe with a correlation coefficient
greater than the threshold. Removing collinear features can help a model
to generalize and improves the interpretability of the model.
Inputs:
x: features dataframe
threshold: features with correlations greater than this value are removed
Output:
dataframe that contains only the non-highly-collinear features
'''
# Calculate the correlation matrix
corr_matrix = x.corr()
iters = range(len(corr_matrix.columns) - 1)
drop_cols = []
# Iterate through the correlation matrix and compare correlations
for i in iters:
for j in range(i+1):
item = corr_matrix.iloc[j:(j+1), (i+1):(i+2)]
col = item.columns
row = item.index
val = abs(item.values)
# If correlation exceeds the threshold
if val >= threshold:
# Print the correlated features and the correlation value
# print(col.values[0], "|", row.values[0], "|", round(val[0][0], 2))
drop_cols.append(col.values[0])
# Drop one of each pair of correlated columns
drops = set(drop_cols)
x = x.drop(columns=drops)
return drops
# References:
# https://towardsdatascience.com/the-search-for-categorical-correlation-a1cf7f1888c9
# https://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V
def cramers_v(x, y):
""" calculate Cramers V statistic for categorial-categorial association.
uses correction from Bergsma and Wicher,
Journal of the Korean Statistical Society 42 (2013): 323-328
"""
confusion_matrix = pd.crosstab(x,y)
chi2 = sts.chi2_contingency(confusion_matrix)[0]
n = confusion_matrix.sum().sum()
phi2 = chi2/n
r,k = confusion_matrix.shape
phi2corr = max(0, phi2-((k-1)*(r-1))/(n-1))
rcorr = r-((r-1)**2)/(n-1)
kcorr = k-((k-1)**2)/(n-1)
return np.sqrt(phi2corr/min((kcorr-1),(rcorr-1)))
#outlier functions
def simplify_column(col, df, threshold=0.005, value='mode'):
df[col] = df[col].replace(df[col].value_counts(dropna=True)[df[col].value_counts(dropna=True, normalize=True)<threshold].index,df[col].mode()[0] if value=='mode' else 'other')
return df[col]
def identify_collinear_categorical_features(df, columns, threshold):
"""
Objective:
Identify collinear categorical features in a dataframe with Cramér's V greater than the threshold.
Inputs:
df: dataframe
columns: list of column names to check for collinearity
threshold: features with Cramér's V greater than this value are considered collinear
Output:
list of columns to drop
"""
# Create an empty DataFrame to store the results
cramers_v_matrix = pd.DataFrame(index=columns, columns=columns, dtype=float)
# Fill in the Cramér's V values for each pair of columns
for col1 in columns:
for col2 in columns:
cramers_v_matrix.loc[col1, col2] = cramers_v(df[col1], df[col2])
# Identify columns to drop based on Cramér's V threshold
drop_cols = set()
for i, col1 in enumerate(columns):
for j, col2 in enumerate(columns):
if i < j and cramers_v_matrix.loc[col1, col2] > threshold:
drop_cols.add(col2)
return list(drop_cols)
def remove_collinear_categorical_features(df, drop_cols):
"""
Objective:
Remove collinear categorical features from a dataframe.
Inputs:
df: dataframe
drop_cols: list of columns to drop
Output:
dataframe that contains only the non-highly-collinear features
"""
# Drop the identified columns
df = df.drop(columns=drop_cols)
return df
#Encoders
# Frequency Encoding
def frequency_encoding(train, test, columns, self_encoding=False):
for col in columns:
df = pd.concat([train[[col]], test[[col]]])
fq_encode = df[col].value_counts(dropna=False, normalize=True).to_dict()
if self_encoding:
train[col] = train[col].map(fq_encode)
test[col] = test[col].map(fq_encode)
else:
train[col+'_freq_encoded'] = train[col].map(fq_encode)
test[col+'_freq_encoded'] = test[col].map(fq_encode)
return train, test
#Modeling
def plot_feature_importances(model, num=10, figsize=(20,10)):
feature_imp = pd.Series(model.feature_importances_,index=X.columns).sort_values(ascending=False)[:num]
plt.figure(figsize=figsize)
sns.barplot(x=feature_imp, y=feature_imp.index)
plt.title("Feature Importance")
plt.show()
def remove_collinear_features(x, threshold):
'''
Objective:
Remove collinear features in a dataframe with a correlation coefficient
greater than the threshold. Removing collinear features can help a model
to generalize and improves the interpretability of the model.
Inputs:
x: features dataframe
threshold: features with correlations greater than this value are removed
Output:
dataframe that contains only the non-highly-collinear features
'''
# Calculate the correlation matrix
corr_matrix = x.corr()
iters = range(len(corr_matrix.columns) - 1)
drop_cols = []
# Iterate through the correlation matrix and compare correlations
for i in iters:
for j in range(i+1):
item = corr_matrix.iloc[j:(j+1), (i+1):(i+2)]
col = item.columns
row = item.index
val = abs(item.values)
# If correlation exceeds the threshold
if val >= threshold:
# Check distinct values for each correlated pair
distinct_values_col = len(x[col[0]].unique())
distinct_values_row = len(x[row[0]].unique())
# Keep the one with more distinct values
if distinct_values_col > distinct_values_row:
drop_cols.append(row.values[0])
else:
drop_cols.append(col.values[0])
# Drop one of each pair of correlated columns
drops = set(drop_cols)
x = x.drop(columns=drops)
return drops
# Importing transaction data
# We are standardizing the column types in accordance with the data definition.
# Define column names for the dataset
cols_t = ['TransactionID', 'TransactionDT', 'TransactionAmt',
'ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5', 'card6',
'addr1', 'addr2', 'dist1', 'dist2', 'P_emaildomain', 'R_emaildomain',
'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11',
'C12', 'C13', 'C14', 'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8',
'D9', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15', 'M1', 'M2', 'M3', 'M4',
'M5', 'M6', 'M7', 'M8', 'M9']
# Generate column names for the 'V' features (V1 to V339)
cols_v = ['V'+str(x) for x in range(1, 340)]
# Define data types for the 'V' features as float32
types_v = {c: 'float32' for c in cols_v}
# Specify the columns that need to be converted to the 'object' data type
columns_to_convert_to_object = ['ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5', 'card6',
'addr1', 'addr2', 'P_emaildomain', 'R_emaildomain', 'M1', 'M2', 'M3', 'M4',
'M5', 'M6', 'M7', 'M8', 'M9']
# Read the data from the CSV file into a DataFrame (train)
transaction = pd.read_csv(r'C:\Fraud_Data\data\train_transaction.csv',
usecols=cols_t+['isFraud']+cols_v,
dtype={**types_v, **{col: 'object' for col in columns_to_convert_to_object}}, index_col='TransactionID')
# The `TransactionDT` feature represents a timedelta from a specific reference datetime, rather than an actual timestamp.
# It measures the time elapsed since the reference datetime in a timedelta format.
# Getting a real format of transaction date
# Predefined start date
START_DATE = datetime.datetime.strptime('2017-11-30', '%Y-%m-%d')
# Define the date range
dates_range = pd.date_range(start='2017-10-01', end='2019-01-01')
us_holidays = calendar().holidays(start=dates_range.min(), end=dates_range.max())
# Create 'DT' column using the 'TransactionDT' column
transaction['DT'] = transaction['TransactionDT'].apply(lambda x: (START_DATE + datetime.timedelta(seconds=x)))
# Convert 'DT' column to 'DatetimeIndex' object
transaction['DT'] = pd.to_datetime(transaction['DT'])
# Sorting the DataFrame based on the 'DT' column
transaction = transaction.sort_values(by='DT')
# Splitting train-test (TRAIN 75% TEST 25%) We will then merge these tables with identity table by using TransactionID.
train_index = transaction.index[:3 * len(transaction) // 4]
test_index = transaction.index[3 * len(transaction) // 4:]
# Splitting train_transaction and test_transaction based on indices
train_transaction = transaction.loc[train_index]
test_transaction = transaction.loc[test_index]
train_transaction.head()
| isFraud | TransactionDT | TransactionAmt | ProductCD | card1 | card2 | card3 | card4 | card5 | card6 | ... | V331 | V332 | V333 | V334 | V335 | V336 | V337 | V338 | V339 | DT | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TransactionID | |||||||||||||||||||||
| 2987000 | 0 | 86400 | 68.5 | W | 13926 | NaN | 150.0 | discover | 142.0 | credit | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2017-12-01 00:00:00 |
| 2987001 | 0 | 86401 | 29.0 | W | 2755 | 404.0 | 150.0 | mastercard | 102.0 | credit | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2017-12-01 00:00:01 |
| 2987002 | 0 | 86469 | 59.0 | W | 4663 | 490.0 | 150.0 | visa | 166.0 | debit | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2017-12-01 00:01:09 |
| 2987003 | 0 | 86499 | 50.0 | W | 18132 | 567.0 | 150.0 | mastercard | 117.0 | debit | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2017-12-01 00:01:39 |
| 2987004 | 0 | 86506 | 50.0 | H | 4497 | 514.0 | 150.0 | mastercard | 102.0 | credit | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2017-12-01 00:01:46 |
5 rows × 394 columns
# Importing identity data
# Define column names for the dataset
cols_t = ['TransactionID','DeviceInfo','DeviceType','id_38','id_37','id_36','id_35','id_34','id_33','id_32','id_31','id_30','id_29','id_28',
'id_27','id_26','id_25','id_24','id_23','id_22','id_21','id_20','id_19','id_18','id_17','id_16','id_15','id_14','id_13',
'id_12','id_11','id_10','id_09','id_08','id_07','id_06','id_05','id_04','id_03','id_02','id_01']
# Specify the columns that need to be converted to the 'object' data type
columns_to_convert_to_object = ['DeviceInfo','DeviceType','id_38','id_37','id_36','id_35','id_34','id_33','id_32','id_31','id_30','id_29','id_28',
'id_27','id_26','id_25','id_24','id_23','id_22','id_21','id_20','id_19','id_18','id_17','id_16','id_15','id_14','id_13',
'id_12']
# Read the data
identity = pd.read_csv(
r'C:\Fraud_Data\data\train_identity.csv',
usecols=cols_t,
dtype=dict.fromkeys(columns_to_convert_to_object, 'object'),
index_col='TransactionID'
)
identity.head()
| id_01 | id_02 | id_03 | id_04 | id_05 | id_06 | id_07 | id_08 | id_09 | id_10 | ... | id_31 | id_32 | id_33 | id_34 | id_35 | id_36 | id_37 | id_38 | DeviceType | DeviceInfo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TransactionID | |||||||||||||||||||||
| 2987004 | 0.0 | 70787.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | samsung browser 6.2 | 32.0 | 2220x1080 | match_status:2 | T | F | T | T | mobile | SAMSUNG SM-G892A Build/NRD90M |
| 2987008 | -5.0 | 98945.0 | NaN | NaN | 0.0 | -5.0 | NaN | NaN | NaN | NaN | ... | mobile safari 11.0 | 32.0 | 1334x750 | match_status:1 | T | F | F | T | mobile | iOS Device |
| 2987010 | -5.0 | 191631.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | 0.0 | 0.0 | ... | chrome 62.0 | NaN | NaN | NaN | F | F | T | T | desktop | Windows |
| 2987011 | -5.0 | 221832.0 | NaN | NaN | 0.0 | -6.0 | NaN | NaN | NaN | NaN | ... | chrome 62.0 | NaN | NaN | NaN | F | F | T | T | desktop | NaN |
| 2987016 | 0.0 | 7460.0 | 0.0 | 0.0 | 1.0 | 0.0 | NaN | NaN | 0.0 | 0.0 | ... | chrome 62.0 | 24.0 | 1280x800 | match_status:2 | T | F | T | T | desktop | MacOS |
5 rows × 40 columns
# Check for duplicated Transaction IDs when TransactionID is the index - all transaction ids unique in identity table, this will be elobrated while merging the datasets
print('Length of Transaction IDs:', identity.index.shape[0])
print('Number of unique Transaction IDs:', identity.index.nunique())
Length of Transaction IDs: 144233 Number of unique Transaction IDs: 144233
# Merging datas
# Merge train_transaction and identity
train = pd.merge(train_transaction, identity, how='left', left_index=True, right_index=True)
# Merge test_transaction and identity
test = pd.merge(test_transaction, identity, how='left', left_index=True, right_index=True)
print("Train: ", train.shape)
print("Test: ", test.shape)
# Delete transaction, train_transaction, test_transaction, identity
del transaction, train_transaction, test_transaction, identity
Train: (442905, 434) Test: (147635, 434)
# Train start-end date
print('min Transaction Date: ',min(train['DT'].values))
print('max Transaction Date: ',max(train['DT'].values))
min Transaction Date: 2017-12-01T00:00:00.000000000 max Transaction Date: 2018-04-09T04:03:25.000000000
# Test start-end date
print('min Transaction Date: ',min(test['DT'].values))
print('max Transaction Date: ',max(test['DT'].values))
min Transaction Date: 2018-04-09T04:04:25.000000000 max Transaction Date: 2018-05-31T23:58:51.000000000
# Check for the duplicated dates-train
print('length of Transaction Date',train['DT'].shape[0] )
print('length of unique Transaction Date', train['DT'].nunique())
length of Transaction Date 442905 length of unique Transaction Date 429087
# Check for the duplicated dates-test
print('length of Transaction Date',test['DT'].shape[0] )
print('length of unique Transaction Date', test['DT'].nunique())
length of Transaction Date 147635 length of unique Transaction Date 144262
train.head()
| isFraud | TransactionDT | TransactionAmt | ProductCD | card1 | card2 | card3 | card4 | card5 | card6 | ... | id_31 | id_32 | id_33 | id_34 | id_35 | id_36 | id_37 | id_38 | DeviceType | DeviceInfo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TransactionID | |||||||||||||||||||||
| 2987000 | 0 | 86400 | 68.5 | W | 13926 | NaN | 150.0 | discover | 142.0 | credit | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2987001 | 0 | 86401 | 29.0 | W | 2755 | 404.0 | 150.0 | mastercard | 102.0 | credit | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2987002 | 0 | 86469 | 59.0 | W | 4663 | 490.0 | 150.0 | visa | 166.0 | debit | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2987003 | 0 | 86499 | 50.0 | W | 18132 | 567.0 | 150.0 | mastercard | 117.0 | debit | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2987004 | 0 | 86506 | 50.0 | H | 4497 | 514.0 | 150.0 | mastercard | 102.0 | credit | ... | samsung browser 6.2 | 32.0 | 2220x1080 | match_status:2 | T | F | T | T | mobile | SAMSUNG SM-G892A Build/NRD90M |
5 rows × 434 columns
# Performing garbage collection to release memory occupied by unused objects
gc.collect()
1496
#pickling datasets
#Save 'train' data to a pickle file named 'train_1.pkl'
train.to_pickle(r'C:\Fraud_Data\data\train_1.pkl')
#save 'test' data to a pickle file named 'test_1.pkl'
test.to_pickle(r'C:\Fraud_Data\data\test_1.pkl')
# Read the 'train_1.pkl' pickle file and load it into the 'train' DataFrame
train = pd.read_pickle('./train_1.pkl')
# Read the 'test_1.pkl' pickle file and load it into the 'test' DataFrame
test = pd.read_pickle('./test_1.pkl')
# Just to preserve the original train I did below. original train will be now train_original
# Copy the DataFrame and rename it
train_copy = train.copy() # Create a copy of the original DataFrame and name it train_copy
train_original = train.copy() # Create another copy of the original DataFrame and name it train_original
# Rename train by assigning the copy's content to it
train = train_copy.copy() # Rename train by assigning the content of train_copy to it
The original dataset is characterized by a significant class imbalance, predominantly consisting of non-fraudulent transactions. This imbalance, where only 3.5% of transactions are labeled as fraud, poses a challenge for developing accurate predictive models and conducting analyses. Utilizing such an imbalanced dataset as the basis for machine learning models may lead to substantial errors and the risk of overfitting.
The risk of overfitting arises from the potential for algorithms to incorrectly assume that the majority of transactions are not fraudulent. In contrast to making assumptions, the primary objective is to develop models capable of identifying patterns indicative of fraudulent activities.
In the machine learning context, class imbalance denotes a considerable disparity in the number of data points representing different classes. Addressing this imbalance is paramount to ensure that models are not misled by the prevalence of non-fraudulent instances. The goal is to enable models to accurately identify patterns associated with fraudulent transactions.
In the training set, approximately 96.49% of transactions are labeled as non-fraudulent (isFraud==0), while around 3.51% are identified as fraudulent (isFraud==1). Similarly, in the test set, approximately 96.55% of transactions are non-fraudulent, and about 3.45% are labeled as fraudulent.
This class distribution indicates a high prevalence of non-fraudulent transactions in both the training and test sets, with a relatively small percentage of transactions being classified as fraudulent. This reinforces the presence of a significant class imbalance, which should be considered when developing and evaluating predictive models.
# Train Data
# Count the occurrences of each class in the 'isFraud' column
class_counts = train['isFraud'].value_counts()
# Calculate the percentage distribution
class_percentages = class_counts / len(train) * 100
# Plot the class distribution using Matplotlib
plt.figure(figsize=(8, 6))
sns.barplot(x=class_counts.index, y=class_counts.values, palette="mako")
# Adding percentages above the bars
for i, value in enumerate(class_counts.values):
plt.text(i, value + 50, f'{class_percentages[i]:.2f}%', ha='center', va='bottom', fontsize=10, color='black')
plt.title("Train Data Imbalance - isFraud")
plt.xlabel("Class")
plt.ylabel("Count")
plt.show()
# Test Data
# Count the occurrences of each class in the 'isFraud' column
class_counts = test['isFraud'].value_counts()
# Calculate the percentage distribution
class_percentages = class_counts / len(test) * 100
# Plot the class distribution using Matplotlib
plt.figure(figsize=(8, 6))
sns.barplot(x=class_counts.index, y=class_counts.values, palette="mako")
# Adding percentages above the bars
for i, value in enumerate(class_counts.values):
plt.text(i, value + 50, f'{class_percentages[i]:.2f}%', ha='center', va='bottom', fontsize=10, color='black')
plt.title("Test Data Imbalance - isFraud")
plt.xlabel("Class")
plt.ylabel("Count")
plt.show()
del class_counts
gc.collect()
5669
Datasets have too much missing values.
# Total missing values of the train data
missing_count = train.isnull().sum()
cell_counts = np.product(train.shape)
missing_sum = missing_count.sum()
print ("%",(round(missing_sum/cell_counts,2)) * 100)
% 45.0
# Total missing values of the test data
missing_count = test.isnull().sum()
cell_counts = np.product(test.shape)
missing_sum = missing_count.sum()
print ("%",(round(missing_sum/cell_counts,2)) * 100)
% 45.0
# columns with no nulls of train (20 columns have no nulls, rest have)
null_counts = train.isnull().sum()
columns_with_no_null = null_counts[null_counts == 0].index
print("\nColumns with No Null Values-train:")
print(columns_with_no_null)
Columns with No Null Values-train:
Index(['isFraud', 'TransactionDT', 'TransactionAmt', 'ProductCD', 'card1',
'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11',
'C12', 'C13', 'C14', 'DT'],
dtype='object')
# columns with no nulls of test
null_counts = test.isnull().sum()
columns_with_no_null = null_counts[null_counts == 0].index
print("\nColumns with No Null Values-test:")
print(columns_with_no_null)
Columns with No Null Values-test:
Index(['isFraud', 'TransactionDT', 'TransactionAmt', 'ProductCD', 'card1',
'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11',
'C12', 'C13', 'C14', 'V279', 'V280', 'V284', 'V285', 'V286', 'V287',
'V290', 'V291', 'V292', 'V293', 'V294', 'V295', 'V297', 'V298', 'V299',
'V302', 'V303', 'V304', 'V305', 'V306', 'V307', 'V308', 'V309', 'V310',
'V311', 'V312', 'V316', 'V317', 'V318', 'V319', 'V320', 'V321', 'DT'],
dtype='object')
del missing_count, cell_counts, missing_sum, null_counts, columns_with_no_null
It has been observed that a significant number of columns exhibit a pattern of "correlated missing values," where certain rows share missing values in corresponding positions across multiple columns. Most of them are in the same variable group, this will be a clue for highly correlated variables in the same groups to eliminate later.
# Check for null percentage correlated columns-train
# Create a dictionary to store columns with null percentages > 10%
null_percentage_dict = {}
nan_counts = train.isnull().sum()
for col in train.columns:
null_percent = (nan_counts[col] / len(train)) * 100
if null_percent >= 10:
null_percentage_dict[null_percent] = null_percentage_dict.get(null_percent, []) + [col]
# Sort the dictionary by null percentages
sorted_null_percentage_dict = {k: v for k, v in sorted(null_percentage_dict.items(), reverse=True)}
for null_percent, columns in sorted_null_percentage_dict.items():
print(f"####### Null Percentage = {null_percent:.2f}%")
print(columns)
####### Null Percentage = 99.16% ['id_24'] ####### Null Percentage = 99.08% ['id_25'] ####### Null Percentage = 99.08% ['id_07', 'id_08'] ####### Null Percentage = 99.08% ['id_26'] ####### Null Percentage = 99.08% ['id_21'] ####### Null Percentage = 99.08% ['id_22', 'id_23', 'id_27'] ####### Null Percentage = 93.60% ['D7'] ####### Null Percentage = 93.11% ['dist2'] ####### Null Percentage = 91.95% ['id_18'] ####### Null Percentage = 89.48% ['D13'] ####### Null Percentage = 89.29% ['D14'] ####### Null Percentage = 88.68% ['D12'] ####### Null Percentage = 88.37% ['id_03', 'id_04'] ####### Null Percentage = 87.44% ['D6'] ####### Null Percentage = 86.74% ['D8', 'D9', 'id_09', 'id_10'] ####### Null Percentage = 86.57% ['id_33'] ####### Null Percentage = 85.60% ['id_30'] ####### Null Percentage = 85.60% ['id_32'] ####### Null Percentage = 85.59% ['id_34'] ####### Null Percentage = 85.14% ['id_14'] ####### Null Percentage = 84.82% ['V138', 'V139', 'V140', 'V141', 'V142', 'V143', 'V144', 'V145', 'V146', 'V147', 'V148', 'V149', 'V150', 'V151', 'V152', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V159', 'V160', 'V161', 'V162', 'V163', 'V164', 'V165', 'V166'] ####### Null Percentage = 84.75% ['V322', 'V323', 'V324', 'V325', 'V326', 'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339'] ####### Null Percentage = 78.49% ['DeviceInfo'] ####### Null Percentage = 77.38% ['id_13'] ####### Null Percentage = 76.65% ['id_16'] ####### Null Percentage = 76.23% ['V217', 'V218', 'V219', 'V223', 'V224', 'V225', 'V226', 'V228', 'V229', 'V230', 'V231', 'V232', 'V233', 'V235', 'V236', 'V237', 'V240', 'V241', 'V242', 'V243', 'V244', 'V246', 'V247', 'V248', 'V249', 'V252', 'V253', 'V254', 'V257', 'V258', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V269', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278'] ####### Null Percentage = 75.56% ['R_emaildomain'] ####### Null Percentage = 75.29% ['id_05', 'id_06'] ####### Null Percentage = 74.86% ['V167', 'V168', 'V172', 'V173', 'V176', 'V177', 'V178', 'V179', 'V181', 'V182', 'V183', 'V186', 'V187', 'V190', 'V191', 'V192', 'V193', 'V196', 'V199', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216'] ####### Null Percentage = 74.84% ['V169', 'V170', 'V171', 'V174', 'V175', 'V180', 'V184', 'V185', 'V188', 'V189', 'V194', 'V195', 'V197', 'V198', 'V200', 'V201', 'V208', 'V209', 'V210'] ####### Null Percentage = 74.84% ['id_20'] ####### Null Percentage = 74.83% ['id_19'] ####### Null Percentage = 74.83% ['id_17'] ####### Null Percentage = 74.66% ['id_31'] ####### Null Percentage = 74.62% ['DeviceType'] ####### Null Percentage = 74.61% ['id_02'] ####### Null Percentage = 74.59% ['id_11', 'id_15', 'id_28', 'id_29', 'id_35', 'id_36', 'id_37', 'id_38'] ####### Null Percentage = 74.50% ['V220', 'V221', 'V222', 'V227', 'V234', 'V238', 'V239', 'V245', 'V250', 'V251', 'V255', 'V256', 'V259', 'V270', 'V271', 'V272'] ####### Null Percentage = 74.02% ['id_01', 'id_12'] ####### Null Percentage = 64.81% ['M7'] ####### Null Percentage = 64.80% ['M8', 'M9'] ####### Null Percentage = 61.25% ['dist1'] ####### Null Percentage = 60.23% ['M5'] ####### Null Percentage = 53.82% ['D5'] ####### Null Percentage = 53.02% ['D11', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11'] ####### Null Percentage = 51.88% ['M1', 'M2', 'M3'] ####### Null Percentage = 49.40% ['D2'] ####### Null Percentage = 48.26% ['M4'] ####### Null Percentage = 46.54% ['D3'] ####### Null Percentage = 30.45% ['M6'] ####### Null Percentage = 29.41% ['V35', 'V36', 'V37', 'V38', 'V39', 'V40', 'V41', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52'] ####### Null Percentage = 29.41% ['D4'] ####### Null Percentage = 16.33% ['V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V88', 'V89', 'V90', 'V91', 'V92', 'V93', 'V94'] ####### Null Percentage = 16.33% ['D15'] ####### Null Percentage = 15.55% ['P_emaildomain'] ####### Null Percentage = 14.59% ['V53', 'V54', 'V55', 'V56', 'V57', 'V58', 'V59', 'V60', 'V61', 'V62', 'V63', 'V64', 'V65', 'V66', 'V67', 'V68', 'V69', 'V70', 'V71', 'V72', 'V73', 'V74'] ####### Null Percentage = 14.36% ['V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'V29', 'V30', 'V31', 'V32', 'V33', 'V34'] ####### Null Percentage = 14.35% ['D10'] ####### Null Percentage = 11.45% ['addr1', 'addr2']
# Check for null percentage correlated columns-test
# Create a dictionary to store columns with null percentages > 10%
null_percentage_dict = {}
nan_counts = test.isnull().sum()
for col in test.columns:
null_percent = (nan_counts[col] / len(test)) * 100
if null_percent >= 10:
null_percentage_dict[null_percent] = null_percentage_dict.get(null_percent, []) + [col]
# Sort the dictionary by null percentages
sorted_null_percentage_dict = {k: v for k, v in sorted(null_percentage_dict.items(), reverse=True)}
for null_percent, columns in sorted_null_percentage_dict.items():
print(f"####### Null Percentage = {null_percent:.2f}%")
print(columns)
####### Null Percentage = 99.32% ['id_24'] ####### Null Percentage = 99.27% ['id_25'] ####### Null Percentage = 99.26% ['id_21'] ####### Null Percentage = 99.26% ['id_07', 'id_08'] ####### Null Percentage = 99.26% ['id_22', 'id_23', 'id_26', 'id_27'] ####### Null Percentage = 95.18% ['dist2'] ####### Null Percentage = 93.59% ['id_18'] ####### Null Percentage = 92.84% ['D7'] ####### Null Percentage = 90.65% ['id_30'] ####### Null Percentage = 90.65% ['id_32', 'id_33'] ####### Null Percentage = 90.53% ['id_34'] ####### Null Percentage = 90.35% ['id_14'] ####### Null Percentage = 90.11% ['D12'] ####### Null Percentage = 90.04% ['V138', 'V139', 'V140', 'V141', 'V142', 'V146', 'V147', 'V148', 'V149', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V161', 'V162', 'V163'] ####### Null Percentage = 90.04% ['V143', 'V144', 'V145', 'V150', 'V151', 'V152', 'V159', 'V160', 'V164', 'V165', 'V166'] ####### Null Percentage = 90.02% ['D14'] ####### Null Percentage = 89.98% ['V322', 'V323', 'V324', 'V325', 'V326', 'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339'] ####### Null Percentage = 89.95% ['id_03', 'id_04'] ####### Null Percentage = 89.60% ['D13'] ####### Null Percentage = 89.04% ['D8', 'D9', 'id_09', 'id_10'] ####### Null Percentage = 88.09% ['D6'] ####### Null Percentage = 84.16% ['DeviceInfo'] ####### Null Percentage = 82.95% ['V217', 'V218', 'V219', 'V223', 'V224', 'V225', 'V226', 'V228', 'V229', 'V230', 'V231', 'V232', 'V233', 'V235', 'V236', 'V237', 'V240', 'V241', 'V242', 'V243', 'V244', 'V246', 'V247', 'V248', 'V249', 'V252', 'V253', 'V254', 'V257', 'V258', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V269', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278'] ####### Null Percentage = 82.43% ['id_16'] ####### Null Percentage = 81.61% ['id_13'] ####### Null Percentage = 81.41% ['id_05', 'id_06'] ####### Null Percentage = 81.16% ['id_20'] ####### Null Percentage = 81.13% ['id_19'] ####### Null Percentage = 81.12% ['id_17'] ####### Null Percentage = 81.00% ['id_31'] ####### Null Percentage = 80.85% ['V167', 'V168', 'V172', 'V173', 'V176', 'V177', 'V178', 'V179', 'V181', 'V182', 'V183', 'V186', 'V187', 'V190', 'V191', 'V192', 'V193', 'V196', 'V199', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216'] ####### Null Percentage = 80.77% ['V169', 'V170', 'V171', 'V174', 'V175', 'V180', 'V184', 'V185', 'V188', 'V189', 'V194', 'V195', 'V197', 'V198', 'V200', 'V201', 'V208', 'V209', 'V210'] ####### Null Percentage = 80.75% ['DeviceType'] ####### Null Percentage = 80.74% ['id_11', 'id_28', 'id_29'] ####### Null Percentage = 80.74% ['id_02', 'id_15', 'id_35', 'id_36', 'id_37', 'id_38'] ####### Null Percentage = 80.73% ['V220', 'V221', 'V222', 'V227', 'V234', 'V238', 'V239', 'V245', 'V250', 'V251', 'V255', 'V256', 'V259', 'V270', 'V271', 'V272'] ####### Null Percentage = 80.32% ['R_emaildomain'] ####### Null Percentage = 80.23% ['id_01', 'id_12'] ####### Null Percentage = 56.72% ['M5'] ####### Null Percentage = 54.87% ['dist1'] ####### Null Percentage = 48.42% ['D5'] ####### Null Percentage = 45.86% ['M4'] ####### Null Percentage = 42.00% ['D2'] ####### Null Percentage = 40.12% ['M7'] ####### Null Percentage = 40.12% ['M8', 'M9'] ####### Null Percentage = 38.43% ['D3'] ####### Null Percentage = 30.12% ['D11', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11'] ####### Null Percentage = 27.98% ['M1', 'M2', 'M3'] ####### Null Percentage = 26.21% ['V35', 'V36', 'V37', 'V38', 'V39', 'V40', 'V41', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52'] ####### Null Percentage = 26.20% ['D4'] ####### Null Percentage = 23.38% ['M6'] ####### Null Percentage = 17.33% ['P_emaildomain'] ####### Null Percentage = 11.40% ['V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V88', 'V89', 'V90', 'V91', 'V92', 'V93', 'V94'] ####### Null Percentage = 11.38% ['D15'] ####### Null Percentage = 10.17% ['addr1', 'addr2']
# Drop columns by using 3 criteria:
# 1. If a column only has only one distinct value
# 2. If a column has more than 90% null values
# 3. If one of the categories in a column dominates more than 90% of the column
# we are looking for these criterias in train, then dropping the columns from both train and test
# Initialize lists to store columns to be dropped based on different criteria
one_value_cols, many_null_cols, big_top_value_cols = [], [], []
# Iterate through only the train DataFrame
for df in [train]:
# Identify columns with only one distinct value
one_value_cols += [col for col in df.columns if df[col].nunique() == 1]
# Identify columns with more than 90% null values
many_null_cols += [col for col in df.columns if df[col].isnull().sum() / df.shape[0] > 0.9]
# Identify columns where a single value dominates more than 90%
big_top_value_cols += [col for col in df.columns if df[col].value_counts(dropna=False, normalize=True).values[0] > 0.9]
# Combine the lists of columns to be dropped, removing duplicates using set
cols_to_drop = list(set(one_value_cols + many_null_cols + big_top_value_cols))
# Check if 'isFraud' is in the list of columns to be dropped, and remove it if present
if 'isFraud' in cols_to_drop:
cols_to_drop.remove('isFraud')
# Drop the identified columns from the train DataFrame
train = train.drop(cols_to_drop, axis=1)
test = test.drop(cols_to_drop, axis=1)
# Print the number of features that are going to be dropped for being considered useless
print(f'{len(cols_to_drop)} features are going to be dropped for being useless')
66 features are going to be dropped for being useless
# TransactionDT Dist for Train Transaction Data - Whole Data
time_val_whole = train['DT'].values
# Create a figure
plt.figure(figsize=(18, 5))
# Create a distribution plot for TransactionDT column - Train Data
plt.subplot(1, 3, 1) # 1 row, 3 columns, plot at position 1
sns.kdeplot(time_val_whole, color='r', fill=True, edgecolor='black')
plt.title('Distribution of DT - Train Data', fontsize=14)
plt.xlabel('TransactionDT')
# TransactionDT Dist for Train - isFraud=0
time_val_no_fraud = train['DT'][train['isFraud'] == 0]
# TransactionDT Dist for Train - isFraud=1
time_val_fraud = train['DT'][train['isFraud'] == 1]
# Create subplots for isFraud=0 and isFraud=1
for i, time_val in enumerate([time_val_no_fraud, time_val_fraud], start=2):
plt.subplot(1, 3, i)
sns.kdeplot(time_val, color='r', fill=True, edgecolor='black')
plt.title(f'Distribution of DT - isFraud={i-2}', fontsize=14)
plt.xlabel('DT')
# Adjust layout
plt.tight_layout()
# Show the plots
plt.show()
# TransactionDT Dist for Train Transaction Data - Whole Data
time_val_whole = test['DT'].values
# Create a figure
plt.figure(figsize=(18, 5))
# Create a distribution plot for TransactionDT column - Whole Data
plt.subplot(1, 3, 1) # 1 row, 3 columns, plot at position 1
sns.kdeplot(time_val_whole, color='r', fill=True, edgecolor='black')
plt.title('Distribution of DT - Train Data', fontsize=14)
plt.xlabel('TransactionDT')
# TransactionDT Dist for Train Transaction Data - isFraud=0
time_val_no_fraud = test['DT'][test['isFraud'] == 0]
# TransactionDT Dist for Train Transaction Data - isFraud=1
time_val_fraud = test['DT'][test['isFraud'] == 1]
# Create subplots for isFraud=0 and isFraud=1
for i, time_val in enumerate([time_val_no_fraud, time_val_fraud], start=2):
plt.subplot(1, 3, i)
sns.kdeplot(time_val, color='r', fill=True, edgecolor='black')
plt.title(f'Distribution of DT - isFraud={i-2}', fontsize=14)
plt.xlabel('DT')
# Adjust layout
plt.tight_layout()
# Show the plots
plt.show()
column_details(regex='TransactionAmt', df=train)
Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE
TransactionAmt: float64, 17111, %0.0
[2.5100000e-01 2.7200000e-01 2.9200000e-01 ... 6.0852300e+03 6.4509700e+03
3.1937391e+04]
plt.figure(figsize=(12,8))
plt.subplot(121)
sns.stripplot(y='TransactionAmt', x='isFraud', data=train)
plt.axhline(6500, color='red')
plt.title('Train')
plt.subplot(122)
sns.stripplot(y='TransactionAmt', x='isFraud', data=test)
plt.axhline(6500, color='red')
plt.title('Test')
Text(0.5, 1.0, 'Test')
max_transaction_amount = train['TransactionAmt'][train['TransactionAmt'] < 30000].max()
print(f"The maximum transaction amount below 30000 is: {max_transaction_amount}")
The maximum transaction amount below 30000 is: 6450.97
There are outliers in Transaction_Amount with isFraud == 0. These amounts are above 30k. We find the maximum amount below 30k ( 6450.97 ). We will capped the Train transaction with 6450.97 below code.
# Identify transactions with isFraud == 0 and TransactionAmt above 6450.97
condition = train['TransactionAmt'] > 6450.97
# Cap the TransactionAmt at 5368 for the identified transactions
train.loc[condition, 'TransactionAmt'] = 6450.97
train['TransactionAmt'].max()
6450.97
print('Avg Transaction Amount by Frauds-Train', train[train.isFraud==1]['TransactionAmt'].mean())
print('Avg Transaction Amount by Non-Frauds-Train', train[train.isFraud==0]['TransactionAmt'].mean())
print('Avg Transaction Amount-Train',train['TransactionAmt'].mean())
print('Avg Transaction Amount-Test', test['TransactionAmt'].mean() )
Avg Transaction Amount by Frauds-Train 147.32386647818544 Avg Transaction Amount by Non-Frauds-Train 133.7389101726486 Avg Transaction Amount-Train 134.2162646278547 Avg Transaction Amount-Test 137.11464901954145
The averages of TransactionAmt of train and test datasets are nearly same. The average of the fraud transactions(147.32) is bigger than the average of the non-fraud transactions(133.73). The average transaction amount for fraudulent transactions appears to be higher than that for legitimate transactions. Statistically, the average transaction amount for fraudulent transactions is 147.32 units, whereas the average amount for legitimate transactions is 133.74 units. This observation indicates that fraudulent transactions tend to involve higher amounts
#Distribution of TransactionAmt-Train
time_val = train['TransactionAmt'].values
plt.figure(figsize=(18, 4))
sns.distplot(time_val, color='r')
plt.title('Distribution of TransactionAmt-Train', fontsize=14)
plt.xlim([min(time_val)-1000, max(time_val)])
# Show the plot
plt.show()
#Distribution of TransactionAmt-Train
time_val = test['TransactionAmt'].values
plt.figure(figsize=(18, 4))
sns.distplot(time_val, color='r')
plt.title('Distribution of TransactionAmt-Train', fontsize=14)
plt.xlim([min(time_val)-1000, max(time_val)])
# Show the plot
plt.show()
plt.figure(figsize=(15,8))
plt.suptitle('Time of Transaction vs Amount by isFraud')
fraud_mean, nonfraud_mean = train[train.isFraud=='1']['TransactionAmt'].mean(), train[train.isFraud=='0']['TransactionAmt'].mean()
sns.scatterplot(x=train['DT'], y=train['TransactionAmt'], data=train, hue='isFraud', size="isFraud", sizes=(200, 20))
plt.axhline(y=fraud_mean ,color='red',label=f'fraud mean:{round(fraud_mean,2)}')
plt.axhline(y=nonfraud_mean, color='green',label=f'non-froud mean:{round(nonfraud_mean,2)}')
plt.legend()
plt.yscale('log')
plt.show()
for df in [train, test]:
column_details(regex='ProductCD', df=df)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE ProductCD: object, 5, %0.0 ['C' 'H' 'R' 'S' 'W'] Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE ProductCD: object, 5, %0.0 ['C' 'H' 'R' 'S' 'W']
# Distribution of ProductCD column-Train
plot_col('ProductCD', df=train)
train.groupby('ProductCD')['isFraud'].mean()
ProductCD C 0.113070 H 0.045851 R 0.035957 S 0.061235 W 0.020594 Name: isFraud, dtype: float64
Probably their meaning:
'W' has the highest frequency, while 'S' has the lowest. Most fraud activities realized by C product code. (11%) Least fraud activities with W product ccode. (2%) This could indicate that a C product category is more strongly associated with fraud. The probabilities of fraud for other categories (H, R, S, W) are lower, but the contribution of these categories may still be significant.
# Target Encoding For ProductCD (taking into account the average of the target feature in train set)
temp_dict = train.groupby(['ProductCD'])['isFraud'].agg(['mean']).to_dict()['mean']
train['ProductCD_target_encoded'] = train['ProductCD'].replace(temp_dict)
test['ProductCD_target_encoded'] = test['ProductCD'].replace(temp_dict)
# original 'ProductCD' column dropped
train.drop('ProductCD', axis=1, inplace=True)
test.drop('ProductCD', axis=1, inplace=True)
gc.collect()
29506
column_details(regex='^card\d', df=train)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE card1: object, 12485, %0.0 ['1000' '10000' '10003' ... '9997' '9998' '9999'] card2: object, 500, %1.53 ['100.0' '101.0' '102.0' '103.0' '104.0' '105.0' '106.0' '108.0' '109.0' '110.0' '111.0' '112.0' '113.0' '114.0' '115.0' '116.0' '117.0' '118.0' '119.0' '120.0' '121.0' '122.0' '123.0' '124.0' '125.0' '126.0' '127.0' '128.0' '129.0' '130.0' '131.0' '132.0' '133.0' '134.0' '135.0' '136.0' '137.0' '138.0' '139.0' '140.0' '141.0' '142.0' '143.0' '144.0' '145.0' '146.0' '147.0' '148.0' '149.0' '150.0' '151.0' '152.0' '153.0' '154.0' '155.0' '156.0' '157.0' '158.0' '159.0' '160.0' '161.0' '162.0' '163.0' '164.0' '165.0' '166.0' '167.0' '168.0' '169.0' '170.0' '171.0' '172.0' '173.0' '174.0' '175.0' '176.0' '177.0' '178.0' '179.0' '180.0' '181.0' '182.0' '183.0' '184.0' '185.0' '186.0' '187.0' '188.0' '189.0' '190.0' '191.0' '192.0' '193.0' '194.0' '195.0' '196.0' '197.0' '198.0' '199.0' '200.0' '201.0' '202.0' '203.0' '204.0' '205.0' '206.0' '207.0' '208.0' '209.0' '210.0' '211.0' '212.0' '213.0' '214.0' '215.0' '216.0' '217.0' '218.0' '219.0' '220.0' '221.0' '222.0' '223.0' '224.0' '225.0' '226.0' '227.0' '228.0' '229.0' '230.0' '231.0' '232.0' '233.0' '234.0' '235.0' '236.0' '237.0' '238.0' '239.0' '240.0' '241.0' '242.0' '243.0' '244.0' '245.0' '246.0' '247.0' '248.0' '249.0' '250.0' '251.0' '252.0' '253.0' '254.0' '255.0' '256.0' '257.0' '258.0' '259.0' '260.0' '261.0' '262.0' '263.0' '264.0' '265.0' '266.0' '267.0' '268.0' '269.0' '270.0' '271.0' '272.0' '273.0' '274.0' '275.0' '276.0' '277.0' '278.0' '279.0' '280.0' '281.0' '282.0' '283.0' '284.0' '285.0' '286.0' '287.0' '288.0' '289.0' '290.0' '291.0' '292.0' '293.0' '294.0' '295.0' '296.0' '297.0' '298.0' '299.0' '300.0' '301.0' '302.0' '303.0' '304.0' '305.0' '306.0' '307.0' '308.0' '309.0' '310.0' '311.0' '312.0' '313.0' '314.0' '315.0' '316.0' '317.0' '318.0' '319.0' '320.0' '321.0' '322.0' '323.0' '324.0' '325.0' '326.0' '327.0' '328.0' '329.0' '330.0' '331.0' '332.0' '333.0' '334.0' '335.0' '336.0' '337.0' '338.0' '339.0' '340.0' '341.0' '342.0' '343.0' '344.0' '345.0' '346.0' '347.0' '348.0' '349.0' '350.0' '351.0' '352.0' '353.0' '354.0' '355.0' '356.0' '357.0' '358.0' '359.0' '360.0' '361.0' '362.0' '363.0' '364.0' '365.0' '366.0' '367.0' '368.0' '369.0' '370.0' '371.0' '372.0' '373.0' '374.0' '375.0' '376.0' '377.0' '378.0' '379.0' '380.0' '381.0' '382.0' '383.0' '384.0' '385.0' '386.0' '387.0' '388.0' '389.0' '390.0' '391.0' '392.0' '393.0' '394.0' '395.0' '396.0' '397.0' '398.0' '399.0' '400.0' '401.0' '402.0' '403.0' '404.0' '405.0' '406.0' '407.0' '408.0' '409.0' '410.0' '411.0' '412.0' '413.0' '414.0' '415.0' '416.0' '417.0' '418.0' '419.0' '420.0' '421.0' '422.0' '423.0' '424.0' '425.0' '426.0' '427.0' '428.0' '429.0' '430.0' '431.0' '432.0' '433.0' '434.0' '435.0' '436.0' '437.0' '438.0' '439.0' '440.0' '441.0' '442.0' '443.0' '444.0' '445.0' '446.0' '447.0' '448.0' '449.0' '450.0' '451.0' '452.0' '453.0' '454.0' '455.0' '456.0' '457.0' '458.0' '459.0' '460.0' '461.0' '462.0' '463.0' '464.0' '465.0' '466.0' '467.0' '468.0' '469.0' '470.0' '471.0' '472.0' '473.0' '474.0' '475.0' '476.0' '477.0' '478.0' '479.0' '480.0' '481.0' '482.0' '483.0' '484.0' '485.0' '486.0' '487.0' '488.0' '489.0' '490.0' '491.0' '492.0' '493.0' '494.0' '495.0' '496.0' '497.0' '498.0' '499.0' '500.0' '501.0' '502.0' '503.0' '504.0' '505.0' '506.0' '507.0' '508.0' '509.0' '510.0' '511.0' '512.0' '513.0' '514.0' '515.0' '516.0' '517.0' '518.0' '519.0' '520.0' '521.0' '522.0' '523.0' '524.0' '525.0' '526.0' '527.0' '528.0' '529.0' '530.0' '531.0' '532.0' '533.0' '534.0' '535.0' '536.0' '537.0' '538.0' '539.0' '540.0' '541.0' '542.0' '543.0' '544.0' '545.0' '546.0' '547.0' '548.0' '549.0' '550.0' '551.0' '552.0' '553.0' '554.0' '555.0' '556.0' '557.0' '558.0' '559.0' '560.0' '561.0' '562.0' '563.0' '564.0' '565.0' '566.0' '567.0' '568.0' '569.0' '570.0' '571.0' '572.0' '573.0' '574.0' '575.0' '576.0' '577.0' '578.0' '579.0' '580.0' '581.0' '582.0' '583.0' '584.0' '585.0' '586.0' '587.0' '588.0' '589.0' '590.0' '591.0' '592.0' '593.0' '594.0' '595.0' '596.0' '597.0' '598.0' '599.0' '600.0' nan] card3: object, 106, %0.19 ['100.0' '101.0' '102.0' '105.0' '106.0' '107.0' '108.0' '109.0' '111.0' '116.0' '117.0' '118.0' '119.0' '120.0' '121.0' '122.0' '123.0' '124.0' '125.0' '126.0' '127.0' '128.0' '129.0' '130.0' '131.0' '132.0' '133.0' '134.0' '135.0' '136.0' '137.0' '138.0' '141.0' '142.0' '143.0' '144.0' '146.0' '147.0' '148.0' '149.0' '150.0' '152.0' '153.0' '155.0' '156.0' '157.0' '159.0' '160.0' '161.0' '162.0' '163.0' '164.0' '166.0' '167.0' '168.0' '169.0' '170.0' '171.0' '174.0' '176.0' '177.0' '179.0' '180.0' '181.0' '182.0' '183.0' '185.0' '186.0' '188.0' '189.0' '190.0' '191.0' '193.0' '194.0' '195.0' '197.0' '198.0' '199.0' '200.0' '201.0' '202.0' '203.0' '204.0' '205.0' '206.0' '207.0' '208.0' '209.0' '210.0' '211.0' '212.0' '213.0' '214.0' '215.0' '217.0' '219.0' '220.0' '221.0' '222.0' '223.0' '224.0' '225.0' '226.0' '227.0' '229.0' '231.0' nan] card4: object, 4, %0.19 ['american express' 'discover' 'mastercard' 'visa' nan] card5: object, 111, %0.69 ['100.0' '101.0' '102.0' '104.0' '105.0' '106.0' '107.0' '109.0' '111.0' '113.0' '114.0' '115.0' '116.0' '117.0' '118.0' '119.0' '120.0' '121.0' '122.0' '123.0' '125.0' '126.0' '127.0' '128.0' '129.0' '130.0' '131.0' '132.0' '133.0' '134.0' '135.0' '136.0' '137.0' '138.0' '139.0' '140.0' '141.0' '142.0' '143.0' '144.0' '145.0' '146.0' '147.0' '148.0' '149.0' '150.0' '152.0' '156.0' '157.0' '158.0' '159.0' '162.0' '163.0' '164.0' '165.0' '166.0' '167.0' '169.0' '171.0' '172.0' '173.0' '177.0' '178.0' '180.0' '181.0' '182.0' '183.0' '184.0' '185.0' '187.0' '188.0' '189.0' '190.0' '191.0' '194.0' '195.0' '196.0' '197.0' '198.0' '199.0' '200.0' '201.0' '202.0' '203.0' '204.0' '206.0' '207.0' '210.0' '211.0' '212.0' '213.0' '214.0' '215.0' '216.0' '217.0' '219.0' '221.0' '222.0' '223.0' '224.0' '225.0' '226.0' '228.0' '229.0' '230.0' '231.0' '232.0' '233.0' '234.0' '236.0' '237.0' nan] card6: object, 4, %0.19 ['charge card' 'credit' 'debit' 'debit or credit' nan]
cards = ['card1', 'card2', 'card3', 'card4', 'card5', 'card6']
for i in cards:
print ("Unique ",i, " = ",train[i].nunique())
Unique card1 = 12485 Unique card2 = 500 Unique card3 = 106 Unique card4 = 4 Unique card5 = 111 Unique card6 = 4
Card4 and Card6 has 4 distinct value, we will observe the distributions of fraud activities in these columns.
# to show the most used Brand of electronic cards for fraud transactions in the train dataset
plot_col("card4", df=train)
train.groupby('card4')['isFraud'].mean()
card4 american express 0.027966 discover 0.064971 mastercard 0.035260 visa 0.034743 Name: isFraud, dtype: float64
# to show the most used method of transaction with cards for fraud activities in the train dataset
plot_col("card6", df=train)
Card1: The 'Card 1' column, originally designated as categorical, exhibits behavior akin to continuous data, featuring a substantial '13553' unique values.
# identify the differences in the 'card1' column between the 'train' and 'test' DataFrames
# old versions represent the unique values in the 'card1' column that exist in the 'train' but not in the 'test' DataFrame and new versions are vice versa.
old_versions_card1 = set(train['card1'].unique()) - set(test['card1'].unique())
new_versions_card1 = set(test['card1'].unique()) - set(train['card1'].unique())
print("Old versions of card1 (in train but not in test):", old_versions_card1)
print("New versions of card1 (in test but not in train):", new_versions_card1)
Old versions of card1 (in train but not in test): {'6947', '3537', '1844', '6468', '18175', '4671', '7140', '13639', '5359', '11440', '12744', '11327', '14580', '5848', '5568', '14920', '10169', '3403', '2992', '17882', '8716', '4649', '5480', '8261', '6461', '9955', '10628', '16467', '9119', '6842', '6964', '15247', '8026', '3692', '1625', '14663', '12331', '2855', '13878', '11766', '10557', '16556', '17505', '16492', '8529', '1732', '2513', '1001', '5378', '14508', '12441', '2970', '1339', '2433', '6787', '8574', '5328', '16715', '17190', '16938', '9791', '13440', '7024', '9851', '13812', '10899', '6010', '10606', '4890', '18024', '9388', '17163', '15229', '3817', '3860', '13304', '3358', '11742', '9927', '15548', '13212', '8126', '10805', '8045', '5390', '16765', '2086', '15481', '9467', '13709', '17033', '3656', '10739', '1883', '6436', '15660', '3609', '13723', '17801', '7869', '4397', '4762', '3082', '12027', '14333', '9814', '9401', '17448', '11694', '2563', '11823', '2575', '16325', '12344', '6047', '16451', '3324', '12085', '9383', '8337', '13498', '11656', '1636', '7880', '8203', '6421', '2618', '2814', '4602', '16527', '3973', '10154', '2417', '12123', '14133', '9396', '11806', '16018', '11613', '11365', '7414', '7613', '16906', '14151', '2651', '15075', '7491', '8050', '10869', '3970', '2585', '11525', '8266', '14629', '16012', '3340', '17706', '10103', '9460', '7266', '14791', '15727', '17897', '16662', '10514', '8749', '11412', '9201', '11322', '5086', '18235', '9004', '16354', '11320', '14302', '17309', '2735', '16545', '15469', '4120', '11692', '2032', '4230', '8185', '16608', '11032', '9991', '13604', '2654', '7492', '2431', '1583', '8876', '1855', '1118', '8786', '9621', '10977', '2483', '15480', '16835', '7285', '7455', '9427', '16597', '8156', '12056', '3132', '11502', '2510', '11699', '16186', '3243', '1161', '7612', '14570', '6442', '13341', '10249', '8471', '18221', '5866', '11948', '3457', '15949', '8920', '8244', '5953', '2091', '8682', '6549', '1362', '8829', '6646', '8059', '8507', '12566', '16257', '3431', '1348', '13731', '15223', '10441', '3341', '3373', '11940', '8455', '2491', '10530', '16365', '13267', '17437', '14311', '4839', '11316', '9994', '9710', '10782', '5508', '10223', '10599', '4761', '14003', '16324', '15148', '14109', '8840', '13518', '5357', '8082', '18273', '16453', '15059', '15332', '13348', '12461', '9093', '9453', '15241', '12750', '2278', '13854', '6881', '16796', '11355', '15964', '6312', '3626', '13849', '9086', '8813', '2942', '11110', '13974', '9406', '18302', '17853', '10274', '5127', '4620', '6269', '7697', '12068', '12798', '2395', '6760', '16169', '17890', '15302', '2225', '8761', '2832', '13725', '2253', '7217', '6305', '14607', '10371', '7866', '12949', '11484', '6707', '5509', '11714', '1000', '5105', '1007', '15351', '8675', '8363', '8885', '16953', '3787', '17816', '16611', '8114', '10592', '1079', '12900', '1485', '8615', '1704', '1816', '3588', '6001', '3447', '6861', '7200', '16754', '7443', '16877', '11000', '5682', '7717', '8283', '4572', '6975', '9861', '9572', '10890', '16248', '1337', '3163', '4588', '13907', '12040', '14675', '16214', '11321', '6302', '14706', '6863', '2793', '4050', '14832', '1823', '4563', '12466', '12185', '6880', '6639', '14425', '2330', '12355', '6345', '13981', '14900', '15002', '12482', '13022', '15950', '2931', '13987', '9551', '10187', '4336', '13700', '15029', '15760', '7730', '7293', '3177', '1317', '12438', '3336', '5582', '4795', '16068', '6488', '3338', '9794', '5855', '11007', '9782', '4035', '6534', '9452', '2220', '14597', '17206', '13418', '12734', '11872', '11301', '3812', '1185', '15752', '5622', '18051', '6365', '9704', '13772', '7936', '13466', '6330', '15900', '2766', '14615', '6308', '10487', '9899', '2386', '1252', '5477', '5205', '7598', '4224', '5045', '12255', '5117', '15256', '18208', '2321', '8850', '12485', '17512', '17507', '12010', '7278', '8132', '9327', '18154', '13031', '2016', '17809', '5320', '5369', '16097', '9723', '9051', '17778', '18094', '1055', '11688', '1058', '6928', '3294', '10204', '10996', '7390', '1582', '16066', '12079', '12801', '10020', '5281', '7424', '12895', '2712', '16890', '3618', '17011', '14486', '14688', '12799', '12329', '12505', '18004', '13707', '18055', '14314', '10919', '11094', '6710', '8641', '16758', '6403', '11051', '17213', '13471', '9640', '16465', '16368', '10255', '17791', '14188', '7066', '3552', '17181', '1615', '8164', '3712', '3313', '12898', '6258', '13918', '18347', '10562', '5058', '4177', '16747', '2515', '3459', '9377', '13675', '11358', '16941', '18146', '17313', '14592', '8124', '16812', '1156', '12274', '14584', '1889', '13456', '8142', '16766', '12549', '9473', '13922', '15097', '1261', '13828', '12728', '10827', '7042', '6912', '3723', '3838', '2313', '17752', '10884', '15867', '1846', '4539', '5538', '4275', '2226', '2188', '3600', '10042', '3241', '5745', '2291', '11217', '10862', '5821', '9908', '9553', '14654', '13533', '3809', '16300', '12201', '18364', '1944', '4948', '4926', '14440', '2401', '12539', '11786', '13086', '16570', '6633', '17937', '1418', '5440', '2854', '17928', '6132', '11050', '16266', '2709', '1023', '3239', '11642', '9161', '2013', '6929', '15778', '3131', '18184', '16067', '15826', '8513', '9028', '7872', '6056', '3831', '18108', '11254', '3765', '16094', '4009', '17700', '14564', '1221', '14923', '7726', '18043', '15393', '9957', '3728', '7839', '6234', '13805', '14760', '2420', '15870', '13082', '11889', '4055', '13744', '6427', '17021', '6578', '1205', '12740', '11134', '1973', '15353', '4574', '7139', '12011', '9111', '11395', '9120', '9484', '3321', '13475', '7406', '3114', '11188', '7363', '10929', '15601', '1510', '9027', '16439', '12242', '17278', '8307', '16678', '4289', '5059', '1264', '2105', '16830', '17960', '11403', '8515', '1277', '8605', '14986', '6445', '6559', '17352', '13976', '10870', '5546', '3317', '2778', '9516', '9368', '8561', '6492', '13680', '12003', 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New versions of card1 (in test but not in train): {'11591', '1981', '6006', '11583', '10901', '8344', '14812', '5603', '7783', '9272', '18056', '10320', '8152', '5742', '6966', '17982', '3965', '5019', '3006', '11931', '6858', '17595', '14358', '2570', '9601', '1247', '1026', '9089', '10600', '13361', '1467', '12706', '4804', '4459', '7678', '11842', '16864', '10781', '9877', '1385', '5020', '14210', '17745', '2358', '4669', '17938', '8205', '11271', '14726', '2104', '3021', '16448', '3776', '8134', '6266', '10597', '12814', '7608', '5030', '10173', '6440', '9935', '3823', '7761', '2514', '1900', '16478', '8281', '3616', '7589', '1380', '12611', '11435', '13942', '4313', '1952', '13845', '18079', '14701', '10560', '9115', '7818', '6839', '12375', '6950', '14450', '13447', '8975', '17664', '7734', '17339', '13872', '11928', '13835', '17597', '15645', '6391', '15150', '13074', '7647', '10326', '7597', '1511', '14029', '11405', '17248', '12502', '16134', '9647', '11695', '1014', '3296', 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'6665', '16223', '6314', '3825', '1872', '12292', '10362', '8062', '2140', '12066', '11490', '1182', '9420', '4896', '1383', '4727', '9374', '10823', '13113', '16082', '16924', '5444', '4365', '17160', '5858', '7558', '8645', '8228', '1099', '17298', '14309', '10963', '11588', '17246', '18330', '1854', '4840', '1766', '13584', '16497', '17815', '9757', '7964', '17068', '12047', '11854', '4550', '12022', '5379', '16741', '8568', '17349', '15690', '1571', '4318', '9859', '6919', '6797', '8654', '2319', '5896', '11772', '12853', '3740', '17695', '2462', '9461', '7072', '3956', '13333', '9636', '13925', '3462', '10671', '12626', '1466', '9598', '3015', '4037', '1215', '3443', '8878', '4986', '16280', '2533', '5932', '4454', '12708', '8996', '9733', '6264', '3255', '2737', '6518', '17062', '8151', '6825', '8144', '5684', '14489', '11166', '10077', '10383', '2004', '2323', '8166', '3183', '10298', '9570', '12622', '2546', '5675', '3363', '17614', '3558', '3922', '14404', '12224', '14703', '13349', '10293', '4897', '13753', '6088', '15529', '10824', '7603', '8634', '15206', '4537', '12432', '2352', '11057', '15414', '17925', '18323', '12596', '10738', '16029', '2775', '10918', '9147', '3951', '14994', '14702', '9148', '15607', '8093', '14957', '10281', '5917', '16442', '7702', '9349', '1948', '10163', '3128', '11361', '17640', '5649', '11076', '17083', '3053', '9481', '8025', '12966', '6009', '4197', '11741', '9671', '16851', '9219', '17997', '14360', '11631', '17428', '4170', '3615', '1926', '5799', '6342', '7059', '14773', '1943', '7384', '6509', '8276', '8297', '4172', '13302', '6198', '1230', '5429', '2149', '6723', '4953', '17146', '2645', '7331', '13871', '3357', '9205', '14888', '3323', '6484', '16942', '8220', '2856', '5464', '8227', '10619', '1733', '10233', '11381', '13800', '17340', '16332', '8326', '11762', '1757', '14587', '3862', '7747', '12504', '8450', '11819', '18038', '5756', '2316', '14476', '8410', '10865', '1840', '7002', '17716', '16598', '12671', '12279', '17837', '3493', '7992', '4582', '16226', '8265', '14056', '17277', '11300', '17912', '9530', '15047', '8950', '1432', '7396', '14445', '3739', '1041', '14999', '7910', '7228', '7763', '10962', '6368', '14434', '15175', '13485', '12406', '18025', '16651', '16625', '17697', '12767', '16083', '8110', '15789', '16078', '14146', '12675', '17323', '6113', '13403', '5176', '7802', '4783', '12333', '2636', '10472', '11437', '7310', '8943', '5279', '3671', '9826', '7341', '14200', '6971', '15758', '2954', '5704', '9217', '13542', '7425', '14844', '7353', '4297', '1947', '7967', '4019', '11439', '17616', '14183', '12971', '10678', '3374', '15749', '1961', '14008', '13166', '1194', '15872', '16700', '16304', '8570', '9788', '5335', '10779', '14359', '4420', '18181', '6121', '7928', '5324', '1703', '2268', '16212', '16288', '7418', '1797', '12864', '4760', '9000', '13399', '1540', '10760', '18284', '2983', '11803', '10364', '17080', '10897', '1491', '6420', '17972', '7933', '2522', '12742', '11549', '5960', '3872', '12456', '12122', '15827', '3859', '15649', '1538', '5528', '2389', '6711', '4895', '4777', '16392', '13286', '4439', '10991', '13635', '11543', '18308', '17994', '18336', '17315', '5534', '6826', '16933', '11353', '6643', '4673', '10793', '18278', '9475', '13779', '14050', '10841', '16719', '11958', '1891', '3942', '10202', '18290', '1640', '2421', '2967', '15972', '3079', '13107', '14458', '10965', '18205', '14707', '13902', '12181', '3415', '16376', '6769', '11252', '14739', '17961', '12752', '15193', '1866', '8710', '9883', '10132', '12112', '12164', '2834', '14416', '1204', '14613', '7849', '5190', '1580', '1053', '9184', '15526', '12315', '5139', '6876', '3894', '1685', '4634', '3061', '12162', '6257', '15456', '11882', '11005', '14091', '14809', '4131', '13197', '1512', '16690', '6664', '1848', '11668', '4626', '8490', '3677', '3497', '10887', '4891', '10730', '11441', '13019', '17845', '11328', '4551', '12661', '11428', '12464', '3339', '10136', '4422', '2161', '3491', '6018', '18313', '9577', '2711', '13654', '13080', '11241', '11091', '3770', '5797', '7536', '9896', '11891', '9532', '12938', '7607', '1032', '12819', '8660', '12328', '18160', '17462', '7189', '2190', '13658', '11220', '3805', '10844', '17989', '7767', '6131', '12202', '6148', '16336', '6650', '10146', '15384', '7261', '7741', '15251', '10422', '8447', '15054', '10556', '5839', '14161', '14381', '17775', '8982', '5697', '9454', '7824', '9405', '12984', '8715', '9910', '6311', '9283', '9792', '4678', '6099', '1126', '5250', '7186', '12197', '2145', '17426', '14350', '5654', '14448', '10826', '6362', '4517', '6682', '13255', '11798', '5847', '11264', '13105', '4903', '7026', '4295', '10525', '4016', '2505', '11767', '2668', '16109', '13938', '10821', '7010', '4412', '8530', '11446', '11495', '4023', '3092', '3073', '6494', '1449', '14593', '15126', '13761', '15117', '16860', '4870', '2788', '1541', '4781', '8299', '13949', '2164', '13394', '4163', '10085', '7317', '12760', '13781', '6469', '7199', '14148', '8896', '10653', '13741', '3420', '16123', '8767', '2174', '5573', '17802', '1351', '12885', '12837', '8985', '6706', '7388', '3513', '13894', '12840', '18352', '9933', '10259', '6298', '10101', '14606', '10656', '2736', '14399', '15490', '9690', '8594', '5776', '13186', '10999', '10290', '14020', '6840', '5269', '8957', '8125', '12183', '17351', '9738', '10324', '11240', '17468', '14777', '8889', '18192', '4363', '17015', '5752', '16553', '11739', '4150', '15974', '1849', '7522', '12585', '13216', '9698', '17627', '6972', '10718', '8366', '2372', '5115', '17629', '6838', '6340', '8145', '13265', '10328', '2780', '9612', '12228', '7227', '15550', '10593', '10477', '8286', '4220', '3098', '17459', '10091', '11610', '13594', '3049', '5227', '8108', '2576', '16615', '17242', '13063', '7421', '10959', '17655', '15049', '8728', '13146', '7087', '18241', '12652', '8963', '5856', '12871', '9211', '16185', '1048', '5375', '12503', '6291', '11765', '14867', '8342', '2859', '15549', '11621', '1040', '15973', '14378', '6320', '10660', '13084', '17476', '3619', '14648'}
len(old_versions_card1), len(new_versions_card1)
(5810, 1068)
# We synchronized test['card1] and train[card1]
# Replace values in 'card1' column of test DataFrame with NaN if they are in new_versions_card1
test['card1'] = test['card1'].apply(lambda x: np.nan if x in new_versions_card1 else x)
# Replace values in 'card1' column of train DataFrame with NaN if they are in old_versions_card1
train['card1'] = train['card1'].apply(lambda x: np.nan if x in old_versions_card1 else x)
# Countplot of the frequency of the unique value frequencies in card1-train
plt.figure(figsize=(30,6))
train.card1.value_counts().to_frame().value_counts().head(100).plot.bar()
<Axes: xlabel='count'>
# Countplot of the frequency of the unique value frequencies in card1-test
plt.figure(figsize=(30,6))
test.card1.value_counts().to_frame().value_counts().head(100).plot.bar()
<Axes: xlabel='count'>
# identify and gather rare values in the 'card1' column in both the 'train' and 'test' DataFrames based on a frequency criterion.
# After identifying these rare values, we collect them into a set (lower frequency means if a category has repeated lower than 3 in the column)
rareCards=[]
for k, df in enumerate([train, test]):
rare_cards = df.card1.value_counts()
rare_cards = rare_cards.where(rare_cards<3).dropna().sort_index().index # ==> rare_cards<3 refers to lower frequency
rareCards += list(rare_cards)
print(f"{('TEST' if k else 'TRAIN')}")
print(f"Number of unique in card1: {df.card1.nunique()}")
print(f"Number of unique values with frequency less than 3 in card1: {len(rare_cards)}\n")
rareCards = set(rareCards)
TRAIN Number of unique in card1: 6675 Number of unique values with frequency less than 3 in card1: 1163 TEST Number of unique in card1: 6675 Number of unique values with frequency less than 3 in card1: 2760
# We replaced "rare card1 values" with Nan. if a card repeated lower than 3
for df in [train, test]:
df['card1'] = df['card1'].apply(lambda x: np.nan if x in rareCards else x)
Card2...Card6
# Same replacement for remaining card columns (card2-card6)
for col in ['card2','card3','card4','card5','card6']:
old_versions_col= set(train[col].unique()) - set(test[col].unique())
new_versions_col = set(test[col].unique()) - set(train[col].unique())
test[col] =test[col].apply(lambda x: np.nan if x in new_versions_col else x)
train[col] =train[col].apply(lambda x: np.nan if x in old_versions_col else x)
rareCards = []
# Specify the range of columns you want to process
columns_to_process = [f'card{i}' for i in range(2, 7)] # Assumes 'card2' to 'card6'
for col in columns_to_process:
for k, df in enumerate([train, test]):
rare_cards = df[col].value_counts()
rare_cards = rare_cards.where(rare_cards < 3).dropna().sort_index().index
rareCards += list(rare_cards)
print(f"{('TEST' if k else 'TRAIN')}")
print(f"Number of unique in {col}: {df[col].nunique()}")
print(f"Number of unique values with frequency less than 3 in {col}: {len(rare_cards)}\n")
rareCards = set(rareCards)
TRAIN Number of unique in card2: 495 Number of unique values with frequency less than 3 in card2: 0 TEST Number of unique in card2: 495 Number of unique values with frequency less than 3 in card2: 1 TRAIN Number of unique in card3: 70 Number of unique values with frequency less than 3 in card3: 5 TEST Number of unique in card3: 70 Number of unique values with frequency less than 3 in card3: 17 TRAIN Number of unique in card4: 4 Number of unique values with frequency less than 3 in card4: 0 TEST Number of unique in card4: 4 Number of unique values with frequency less than 3 in card4: 0 TRAIN Number of unique in card5: 66 Number of unique values with frequency less than 3 in card5: 6 TEST Number of unique in card5: 66 Number of unique values with frequency less than 3 in card5: 8 TRAIN Number of unique in card6: 2 Number of unique values with frequency less than 3 in card6: 0 TEST Number of unique in card6: 2 Number of unique values with frequency less than 3 in card6: 0
# low frequency cats will change into nans
columns_to_process = [f'card{i}' for i in range(2, 7)] # Assumes 'card2' to 'card6'
for col in columns_to_process:
for df in [train, test]:
df[col] = df[col].apply(lambda x: np.nan if x in rareCards else x)
One hot encoding is a common technique used to handle nominal categorical data by converting each category into a binary column. While label encoding can be suitable for ordinal categorical data, it's not the best choice for nominal categorical data due to its inherent ordering. Frequency encoding and target encoding provides an effective solution for nominal categorical data. Because we have lots of distinct values in card1,2,3,5 columns(if we use target encoding most of the values will be too small near to 0 or will be 0) it is better to use frequency encoding by replacing each category with its observed frequency. We will use target encoder for card4(4 distinct values) and card6 (2 distinct values)
# List of columns to process (Number of unique values below 200) (taking into account the average of the target feature in train set)
columns_to_process = ['card3', 'card4', 'card5', 'card6']
# Loop through each column
for col in columns_to_process:
# Calculate target encoding for the current column
temp_dict = train.groupby([col])['isFraud'].agg(['mean']).to_dict()['mean']
# Create a new column with the target encoding values
train[f'{col}_target_encoded'] = train[col].replace(temp_dict)
test[f'{col}_target_encoded'] = test[col].replace(temp_dict)
# Frequency encoding for card1 (Number of unique values above 200)
self_encode_False=['card1', 'card2']
train, test = frequency_encoding(train, test, self_encode_False, self_encoding=False)
# List of original columns to drop
columns_to_drop = ['card1', 'card2', 'card3', 'card4', 'card5', 'card6']
# Drop the original columns
train.drop(columns_to_drop, axis=1, inplace=True)
test.drop(columns_to_drop, axis=1, inplace=True)
Probably addr1 - subzone / add2 - Country
for df in [train, test]:
column_details(regex='addr', df=df)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE addr1: object, 321, %11.45 ['100.0' '101.0' '102.0' '104.0' '105.0' '106.0' '110.0' '111.0' '112.0' '113.0' '117.0' '119.0' '120.0' '122.0' '123.0' '124.0' '125.0' '126.0' '127.0' '128.0' '129.0' '130.0' '131.0' '132.0' '133.0' '134.0' '137.0' '139.0' '141.0' '142.0' '143.0' '144.0' '145.0' '146.0' '148.0' '151.0' '152.0' '153.0' '154.0' '155.0' '156.0' '157.0' '158.0' '159.0' '160.0' '161.0' '162.0' '163.0' '164.0' '166.0' '167.0' '168.0' '170.0' '171.0' '172.0' '174.0' '177.0' '178.0' '180.0' '181.0' '182.0' '183.0' '184.0' '185.0' '187.0' '189.0' '190.0' '191.0' '193.0' '194.0' '195.0' '196.0' '198.0' '199.0' '200.0' '201.0' '202.0' '203.0' '204.0' '205.0' '208.0' '210.0' '211.0' '213.0' '214.0' '215.0' '216.0' '217.0' '218.0' '219.0' '220.0' '221.0' '223.0' '224.0' '225.0' '226.0' '227.0' '231.0' '232.0' '233.0' '234.0' '235.0' '236.0' '239.0' '241.0' '242.0' '243.0' '244.0' '247.0' '248.0' '249.0' '250.0' '251.0' '252.0' '253.0' '254.0' '255.0' '257.0' '258.0' '259.0' '260.0' '261.0' '262.0' '264.0' '265.0' '269.0' '270.0' '272.0' '274.0' '275.0' '276.0' '277.0' '278.0' '279.0' '280.0' '282.0' '283.0' '284.0' '290.0' '292.0' '294.0' '295.0' '296.0' '297.0' '298.0' '299.0' '300.0' '301.0' '302.0' '303.0' '304.0' '305.0' '306.0' '307.0' '308.0' '309.0' '310.0' '312.0' '313.0' '314.0' '315.0' '316.0' '321.0' '322.0' '323.0' '324.0' '325.0' '326.0' '327.0' '328.0' '329.0' '330.0' '331.0' '332.0' '333.0' '335.0' '337.0' '338.0' '339.0' '340.0' '341.0' '343.0' '345.0' '346.0' '347.0' '348.0' '349.0' '351.0' '352.0' '353.0' '356.0' '358.0' '359.0' '360.0' '361.0' '365.0' '366.0' '368.0' '369.0' '371.0' '372.0' '373.0' '374.0' '375.0' '376.0' '377.0' '379.0' '381.0' '382.0' '384.0' '385.0' '386.0' '387.0' '389.0' '390.0' '391.0' '393.0' '395.0' '396.0' '397.0' '399.0' '400.0' '401.0' '402.0' '403.0' '404.0' '406.0' '408.0' '409.0' '410.0' '411.0' '416.0' '417.0' '418.0' '420.0' '425.0' '426.0' '427.0' '428.0' '429.0' '430.0' '431.0' '432.0' '433.0' '434.0' '435.0' '436.0' '439.0' '441.0' '443.0' '444.0' '445.0' '446.0' '448.0' '450.0' '451.0' '452.0' '453.0' '454.0' '456.0' '458.0' '459.0' '462.0' '463.0' '464.0' '465.0' '466.0' '467.0' '468.0' '469.0' '470.0' '471.0' '472.0' '474.0' '476.0' '477.0' '478.0' '479.0' '482.0' '483.0' '485.0' '486.0' '488.0' '489.0' '491.0' '492.0' '493.0' '494.0' '496.0' '498.0' '499.0' '500.0' '501.0' '502.0' '503.0' '504.0' '505.0' '506.0' '508.0' '509.0' '511.0' '512.0' '513.0' '514.0' '515.0' '516.0' '518.0' '519.0' '520.0' '521.0' '522.0' '523.0' '526.0' '527.0' '528.0' '529.0' '530.0' '531.0' '535.0' '536.0' '540.0' nan] addr2: object, 69, %11.45 ['10.0' '101.0' '102.0' '13.0' '14.0' '15.0' '16.0' '17.0' '18.0' '19.0' '20.0' '21.0' '22.0' '23.0' '24.0' '25.0' '26.0' '27.0' '28.0' '29.0' '30.0' '31.0' '32.0' '34.0' '35.0' '36.0' '38.0' '39.0' '40.0' '43.0' '44.0' '46.0' '47.0' '48.0' '49.0' '50.0' '52.0' '54.0' '57.0' '59.0' '60.0' '61.0' '62.0' '63.0' '65.0' '66.0' '68.0' '69.0' '70.0' '71.0' '72.0' '73.0' '74.0' '75.0' '76.0' '77.0' '78.0' '79.0' '82.0' '83.0' '84.0' '86.0' '87.0' '88.0' '89.0' '92.0' '96.0' '97.0' '98.0' nan] Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE addr1: object, 115, %10.17 ['104.0' '105.0' '106.0' '110.0' '122.0' '123.0' '125.0' '126.0' '130.0' '134.0' '143.0' '157.0' '158.0' '159.0' '160.0' '167.0' '170.0' '177.0' '181.0' '184.0' '191.0' '194.0' '199.0' '201.0' '202.0' '203.0' '204.0' '205.0' '206.0' '215.0' '216.0' '220.0' '225.0' '226.0' '231.0' '237.0' '238.0' '239.0' '242.0' '245.0' '247.0' '251.0' '253.0' '254.0' '259.0' '260.0' '264.0' '268.0' '269.0' '272.0' '275.0' '276.0' '277.0' '284.0' '286.0' '296.0' '299.0' '301.0' '303.0' '308.0' '310.0' '315.0' '318.0' '324.0' '325.0' '327.0' '330.0' '337.0' '343.0' '346.0' '348.0' '356.0' '384.0' '387.0' '404.0' '409.0' '410.0' '411.0' '418.0' '420.0' '426.0' '428.0' '432.0' '433.0' '435.0' '436.0' '441.0' '444.0' '445.0' '448.0' '452.0' '453.0' '457.0' '465.0' '469.0' '472.0' '476.0' '478.0' '479.0' '481.0' '483.0' '485.0' '491.0' '492.0' '494.0' '498.0' '499.0' '502.0' '507.0' '508.0' '511.0' '512.0' '515.0' '517.0' '536.0' nan] addr2: object, 27, %10.17 ['100.0' '16.0' '18.0' '19.0' '26.0' '27.0' '31.0' '32.0' '34.0' '40.0' '43.0' '51.0' '54.0' '55.0' '57.0' '59.0' '60.0' '61.0' '62.0' '65.0' '68.0' '78.0' '87.0' '93.0' '94.0' '96.0' '98.0' nan]
train.groupby('addr1')['isFraud'].mean().sort_values(ascending=False).head(20)
addr1 305.0 0.666667 466.0 0.500000 471.0 0.500000 483.0 0.500000 501.0 0.500000 391.0 0.457143 260.0 0.400000 431.0 0.342105 432.0 0.281250 296.0 0.233553 399.0 0.200000 239.0 0.200000 161.0 0.194774 216.0 0.180000 426.0 0.172414 453.0 0.166667 171.0 0.166667 199.0 0.125000 479.0 0.125000 356.0 0.123457 Name: isFraud, dtype: float64
# First ten most frequent adddress1 values in train
print ("Unique Subzones = ",train['addr1'].nunique())
print('\nFirst Ten Address-2')
print('--------------------')
train.addr1.value_counts().head(9)
Unique Subzones = 321 First Ten Address-2 --------------------
addr1 299.0 35042 325.0 31781 204.0 31261 264.0 29697 330.0 18576 315.0 17250 441.0 15527 272.0 15108 123.0 11935 Name: count, dtype: int64
train.groupby('addr2')['isFraud'].mean().sort_values(ascending=False).head(20)
addr2 10.0 1.000000 82.0 1.000000 46.0 1.000000 92.0 1.000000 75.0 1.000000 38.0 0.666667 65.0 0.581081 36.0 0.500000 73.0 0.200000 68.0 0.111111 96.0 0.107011 60.0 0.096003 29.0 0.090909 32.0 0.072289 87.0 0.024207 84.0 0.000000 88.0 0.000000 59.0 0.000000 61.0 0.000000 62.0 0.000000 Name: isFraud, dtype: float64
print ("Unique Countries = ",train['addr2'].nunique())
print('\nFirst Ten Address-2')
print('--------------------')
train.addr2.value_counts().head(9)
Unique Countries = 69 First Ten Address-2 --------------------
addr2 87.0 388434 60.0 2677 96.0 542 32.0 83 65.0 74 16.0 48 31.0 45 19.0 29 26.0 20 Name: count, dtype: int64
# By combining addr1 and addr2 , we create a new addr column
for df in [train, test]:
# Combine 'addr2' and 'addr1' columns as strings, separated by an underscore
df['addr'] = (df['addr2'].astype(str) + '_' + df['addr1'].astype(str)).replace({'nan_nan': np.nan})
train.groupby('addr')['isFraud'].mean().sort_values(ascending=False).head(20)
addr 10.0_296.0 1.000000 46.0_296.0 1.000000 60.0_296.0 1.000000 92.0_296.0 1.000000 82.0_296.0 1.000000 75.0_296.0 1.000000 65.0_296.0 0.704918 38.0_296.0 0.666667 60.0_305.0 0.666667 36.0_296.0 0.500000 60.0_466.0 0.500000 60.0_471.0 0.500000 60.0_501.0 0.500000 60.0_483.0 0.500000 60.0_391.0 0.457143 87.0_260.0 0.400000 60.0_431.0 0.342105 96.0_432.0 0.281250 73.0_296.0 0.250000 60.0_239.0 0.200000 Name: isFraud, dtype: float64
print ("Unique Adresses = ",train['addr'].nunique())
print('\nFirst Ten Addresses')
print('--------------------')
train.addr.value_counts().head(9)
Unique Adresses = 409 First Ten Addresses --------------------
addr 87.0_299.0 35033 87.0_325.0 31780 87.0_204.0 31260 87.0_264.0 29697 87.0_330.0 18573 87.0_315.0 17249 87.0_441.0 15525 87.0_272.0 15107 87.0_123.0 11933 Name: count, dtype: int64
# unique values for address columns in train
train['addr1'].nunique(), train['addr2'].nunique(), train['addr'].nunique()
(321, 69, 409)
# unique values for address columns in train
test['addr1'].nunique(), test['addr2'].nunique(), test['addr'].nunique()
(116, 28, 147)
# Frequency encoding for addr columns
self_encode_False=['addr']
train, test = frequency_encoding(train, test, self_encode_False, self_encoding=False)
self_encode_False=['addr1']
train, test = frequency_encoding(train, test, self_encode_False, self_encoding=False)
self_encode_False=['addr2']
train, test = frequency_encoding(train, test, self_encode_False, self_encoding=False)
# List of original columns to drop
columns_to_drop = ['addr', 'addr1', 'addr2']
# Drop the original columns
train.drop(columns_to_drop, axis=1, inplace=True)
test.drop(columns_to_drop, axis=1, inplace=True)
Top 'addr1' Values by Fraud Rate: The 'addr1' values with the highest fraud rates are 305.0, 466.0, 471.0, 483.0, and 501.0. For example, transactions with 'addr1' equal to 305.0 have a fraud rate of 66.67%.
Top 'addr2' Values by Fraud Rate: The 'addr2' values with the highest fraud rates are 10.0, 82.0, 46.0, 92.0, and 75.0. For example, transactions with 'addr2' equal to 10.0 have a fraud rate of 100%.
Top Combined 'addr' Values by Fraud Rate: The combined 'addr' values (created by concatenating 'addr2' and 'addr1') with the highest fraud rates are 10.0_296.0, 46.0_296.0, 60.0_296.0, 92.0_296.0, and 82.0_296.0. For example, transactions with 'addr' equal to 10.0_296.0 have a fraud rate of 100%.
Insights: Certain combinations of 'addr1' and 'addr2' or the combined 'addr' exhibit higher fraud rates. For instance, the combination 10.0_296.0 appears to have a consistent fraud rate of 100% across 'addr1' and 'addr2'. These patterns may indicate potential areas of interest for further investigation or feature engineering. High fraud rates in specific 'addr' combinations could be indicative of fraudulent behavior or anomalies in those locations.
column_details(regex='^dist', df=df)
Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE
dist1: float64, 1744, %54.87
[0.000e+00 1.000e+00 2.000e+00 ... 7.136e+03 8.081e+03 nan]
plt.figure(figsize=(12,8))
plt.subplot(121)
sns.stripplot(y='dist1', x='isFraud', data=train)
plt.axhline(5000, color='red')
plt.title('Train')
plt.subplot(122)
sns.stripplot(y='dist1', x='isFraud', data=test)
plt.axhline(5000, color='red')
plt.title('Test')
Text(0.5, 1.0, 'Test')
max_dist1 = train['dist1'][train['dist1'] < 5500].max()
print(f"The maximum dist1 below 5500 is: {max_dist1}")
The maximum dist1 below 5500 is: 5431.0
There are outliers in dist1 with isFraud == 0. These are above 5500. We find the maximum amount below 5500 ( 5431 ). We will capped the Train with 5432 below code.
# Identify transactions with isFraud == 0 and dist1 5431
condition = train['dist1'] > 5431
# Cap the dist1 at 5432 for the identified transactions
train.loc[condition, 'dist1'] = 5431
train['dist1'].max()
5431.0
P_emaildomain : categoric, 56 uniques It's possible to make subgroup feature from it or general group
R_emaildomain : categoric, 59 uniques It's possible to make subgroup feature from it or general group
# R_emaildomain
column_details(regex='R_emaildomain', df=train)
Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE
R_emaildomain: object, 60, %75.56
['aim.com' 'anonymous.com' 'aol.com' 'att.net' 'bellsouth.net'
'cableone.net' 'centurylink.net' 'cfl.rr.com' 'charter.net' 'comcast.net'
'cox.net' 'earthlink.net' 'embarqmail.com' 'frontier.com'
'frontiernet.net' 'gmail' 'gmail.com' 'gmx.de' 'hotmail.co.uk'
'hotmail.com' 'hotmail.de' 'hotmail.es' 'hotmail.fr' 'icloud.com'
'juno.com' 'live.com' 'live.com.mx' 'live.fr' 'mac.com' 'mail.com'
'me.com' 'msn.com' 'netzero.com' 'netzero.net' 'optonline.net'
'outlook.com' 'outlook.es' 'prodigy.net.mx' 'protonmail.com' 'ptd.net'
'q.com' 'roadrunner.com' 'rocketmail.com' 'sbcglobal.net' 'sc.rr.com'
'scranton.edu' 'servicios-ta.com' 'suddenlink.net' 'twc.com'
'verizon.net' 'web.de' 'windstream.net' 'yahoo.co.jp' 'yahoo.co.uk'
'yahoo.com' 'yahoo.com.mx' 'yahoo.de' 'yahoo.es' 'yahoo.fr' 'ymail.com'
nan]
#We produced 2 new columns with mail server and domain for R_emaildomain.
for df in [train, test]:
df['R_emaildomain_1'] = df['R_emaildomain'].fillna('').apply(lambda x: x.split(".")[0]).replace({'':np.nan})
df['R_emaildomain_2'] = df['R_emaildomain'].str.split('.', expand=True).iloc[:,1:].fillna('').apply(lambda x:('.'.join(x)).strip('.'), axis=1).replace({'':np.nan})
# P_emaildomain
column_details(regex='P_emaildomain', df=train)
Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE
P_emaildomain: object, 59, %15.55
['aim.com' 'anonymous.com' 'aol.com' 'att.net' 'bellsouth.net'
'cableone.net' 'centurylink.net' 'cfl.rr.com' 'charter.net' 'comcast.net'
'cox.net' 'earthlink.net' 'embarqmail.com' 'frontier.com'
'frontiernet.net' 'gmail' 'gmail.com' 'gmx.de' 'hotmail.co.uk'
'hotmail.com' 'hotmail.de' 'hotmail.es' 'hotmail.fr' 'icloud.com'
'juno.com' 'live.com' 'live.com.mx' 'live.fr' 'mac.com' 'mail.com'
'me.com' 'msn.com' 'netzero.com' 'netzero.net' 'optonline.net'
'outlook.com' 'outlook.es' 'prodigy.net.mx' 'protonmail.com' 'ptd.net'
'q.com' 'roadrunner.com' 'rocketmail.com' 'sbcglobal.net' 'sc.rr.com'
'servicios-ta.com' 'suddenlink.net' 'twc.com' 'verizon.net' 'web.de'
'windstream.net' 'yahoo.co.jp' 'yahoo.co.uk' 'yahoo.com' 'yahoo.com.mx'
'yahoo.de' 'yahoo.es' 'yahoo.fr' 'ymail.com' nan]
# We produced 2 new columns with mail server and domain P_emaildomain.
for df in [train, test]:
df['P_emaildomain_1'] = df['P_emaildomain'].fillna('').apply(lambda x: x.split(".")[0]).replace({'':np.nan})
df['P_emaildomain_2'] = df['P_emaildomain'].str.split('.', expand=True).iloc[:,1:].fillna('').apply(lambda x:('.'.join(x)).strip('.'), axis=1).replace({'':np.nan})
for col in ['R_emaildomain_2', 'P_emaildomain_2']:
plot_col(col, df=train)
# es has 10% fraud rate
fraud_rates = train.groupby('R_emaildomain_2')['isFraud'].mean().reset_index()
fraud_rates.rename(columns={'isFraud': 'FraudRate'}, inplace=True)
fraud_rates.sort_values(by='FraudRate', ascending=False).head()
| R_emaildomain_2 | FraudRate | |
|---|---|---|
| 6 | es | 0.100000 |
| 2 | com | 0.083204 |
| 3 | com.mx | 0.017406 |
| 7 | fr | 0.015015 |
| 8 | net | 0.009868 |
Two highest fraud activity domains:
es (Spain) Category: The category associated with Spain (es) has a higher fraud rate compared to other categories (%10). This suggests that transactions originating from Spain may require closer scrutiny in terms of fraud detection.
com (United States) Category: The category associated with the United States (com) has a significantly higher fraud rate compared to other categories (%8.32). This may imply the need for careful examination of transactions coming from this geographical region.
for col in ['R_emaildomain_1', 'P_emaildomain_1']:
plot_col(col, df=train)
# Protonmail has 95% fraud rate
fraud_rates = train.groupby('R_emaildomain_1')['isFraud'].mean().reset_index()
fraud_rates.rename(columns={'isFraud': 'FraudRate'}, inplace=True)
fraud_rates.sort_values(by='FraudRate', ascending=False).head()
| R_emaildomain_1 | FraudRate | |
|---|---|---|
| 29 | protonmail | 0.950000 |
| 22 | 0.361111 | |
| 27 | outlook | 0.151882 |
| 18 | icloud | 0.119157 |
| 15 | gmail | 0.113450 |
Two highest fraud activity domains:
ProtonMail Category: The ProtonMail category has a significantly higher fraud rate compared to other categories (%95). This indicates that transactions originating from ProtonMail may pose a higher risk of fraud. Careful scrutiny of transactions associated with such accounts could be crucial.
Mail Category: This general 'mail' category exhibits a higher fraud rate compared to other categories (%36). This suggests that transactions from general email providers should be examined with caution.
# correlation between R_emaildomain_2 and P_emaildomain_2 - train
cramers_v(train.R_emaildomain_2,train.P_emaildomain_2)
0.8926752413893435
# correlation between R_emaildomain_2 and P_emaildomain_2 - test
cramers_v(test.R_emaildomain_2,test.P_emaildomain_2)
0.8813311624433039
# correlation between R_emaildomain_1 and P_emaildomain_1 - train
cramers_v(train.R_emaildomain_1,train.P_emaildomain_1)
0.6060523585208291
# correlation between R_emaildomain_1 and P_emaildomain_1 - test
cramers_v(test.R_emaildomain_1,test.P_emaildomain_1)
0.5873334446544154
# R_emaildomain_2 and P_emaildomain_2 are categorically 90% correlated. So we dropped P_emaildomain_2. (Try vice versa later)
# R_emaildomain_1, P_emaildomain_1, R_emaildomain_2 are staying
train = train.drop(['R_emaildomain','P_emaildomain','P_emaildomain_2'], axis=1)
test = test.drop(['R_emaildomain','P_emaildomain','P_emaildomain_2'], axis=1)
# Target Encoding For R_emaildomain_1, P_emaildomain_1, R_emaildomain_2 (taking into account the average of the target feature in train set)
temp_dict = train.groupby(['R_emaildomain_1'])['isFraud'].agg(['mean']).to_dict()['mean']
train['R_emaildomain_1_target_encoded'] = train['R_emaildomain_1'].replace(temp_dict)
test['R_emaildomain_1_target_encoded'] = test['R_emaildomain_1'].replace(temp_dict)
temp_dict = train.groupby(['P_emaildomain_1'])['isFraud'].agg(['mean']).to_dict()['mean']
train['P_emaildomain_1_target_encoded'] = train['P_emaildomain_1'].replace(temp_dict)
test['P_emaildomain_1_target_encoded'] = test['P_emaildomain_1'].replace(temp_dict)
temp_dict = train.groupby(['R_emaildomain_2'])['isFraud'].agg(['mean']).to_dict()['mean']
train['R_emaildomain_2_target_encoded'] = train['R_emaildomain_2'].replace(temp_dict)
test['R_emaildomain_2_target_encoded'] = test['R_emaildomain_2'].replace(temp_dict)
# List of original columns to drop
columns_to_drop = ['R_emaildomain_1', 'R_emaildomain_2', 'P_emaildomain_1']
# Drop the original columns
train.drop(columns_to_drop, axis=1, inplace=True)
test.drop(columns_to_drop, axis=1, inplace=True)
column_details(regex='^C\d', df=train)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE C1: float64, 1577, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 4.682e+03 4.684e+03 4.685e+03] C2: float64, 1078, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 5.625e+03 5.690e+03 5.691e+03] C4: float64, 1214, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 2.251e+03 2.252e+03 2.253e+03] C5: float64, 309, %0.0 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265. 266. 267. 268. 269. 270. 271. 272. 273. 275. 276. 278. 279. 280. 281. 282. 283. 284. 285. 286. 288. 289. 290. 291. 292. 293. 294. 295. 296. 297. 298. 299. 300. 303. 304. 307. 316. 321. 324. 328. 330. 331. 332. 349.] C6: float64, 1286, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 2.251e+03 2.252e+03 2.253e+03] C7: float64, 1101, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 2.253e+03 2.254e+03 2.255e+03] C8: float64, 1218, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 3.328e+03 3.330e+03 3.331e+03] C9: float64, 203, %0.0 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 202. 203. 204. 205. 207. 210.] C10: float64, 1139, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 3.254e+03 3.256e+03 3.257e+03] C11: float64, 1409, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 3.186e+03 3.187e+03 3.188e+03] C12: float64, 1192, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 3.186e+03 3.187e+03 3.188e+03] C13: float64, 1585, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 2.915e+03 2.917e+03 2.918e+03] C14: float64, 1086, %0.0 [0.000e+00 1.000e+00 2.000e+00 ... 1.426e+03 1.428e+03 1.429e+03]
# Finding highly correlated columns to drop
columns=[col for col in train.columns if re.search('^C\d.*', col)]
corr_treshold = 0.75
drop_col = remove_collinear_features(train[columns],corr_treshold)
drop_col
{'C1', 'C10', 'C11', 'C12', 'C14', 'C2', 'C4', 'C6', 'C7', 'C8', 'C9'}
# Select columns starting with 'C'
columns = [col for col in train.columns if re.search('^C\d.*', col)]
# Create a correlation heatmap using the make_corr function
make_corr(columns, train)
# Only C1 and C5 remained.
train = train.drop(drop_col, axis=1)
test = test.drop(drop_col, axis=1)
'''
# NO outlier for C1 column
plt.figure(figsize=(12,8))
plt.subplot(121)
sns.stripplot(y='C1', x='isFraud', data=train)
plt.axhline(5000, color='red')
plt.title('Train')
plt.subplot(122)
sns.stripplot(y='C1', x='isFraud', data=test)
plt.axhline(5000, color='red')
plt.title('Test')
'''
"\n# NO outlier for C1 column\nplt.figure(figsize=(12,8))\nplt.subplot(121)\nsns.stripplot(y='C1', x='isFraud', data=train)\nplt.axhline(5000, color='red')\nplt.title('Train')\n\nplt.subplot(122)\nsns.stripplot(y='C1', x='isFraud', data=test)\nplt.axhline(5000, color='red')\nplt.title('Test')\n"
# NO outlier for C5 column
plt.figure(figsize=(12,8))
plt.subplot(121)
sns.stripplot(y='C5', x='isFraud', data=train)
plt.axhline(400, color='red')
plt.title('Train')
plt.subplot(122)
sns.stripplot(y='C5', x='isFraud', data=test)
plt.axhline(400, color='red')
plt.title('Test')
Text(0.5, 1.0, 'Test')
for df in [train, test]:
column_details(regex='^D\d.*', df=df)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE D1: float64, 641, %0.06 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265. 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279. 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293. 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307. 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321. 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335. 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349. 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363. 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377. 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391. 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405. 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419. 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433. 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447. 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461. 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475. 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489. 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503. 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517. 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531. 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545. 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559. 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573. 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587. 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601. 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615. 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629. 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. nan] D2: float64, 641, %49.4 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265. 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279. 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293. 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307. 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321. 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335. 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349. 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363. 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377. 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391. 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405. 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419. 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433. 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447. 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461. 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475. 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489. 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503. 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517. 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531. 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545. 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559. 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573. 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587. 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601. 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615. 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629. 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. nan] D3: float64, 603, %46.54 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265. 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279. 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293. 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307. 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321. 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335. 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349. 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363. 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377. 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391. 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405. 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419. 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433. 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447. 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461. 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475. 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489. 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503. 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517. 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531. 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545. 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559. 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 572. 573. 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 589. 591. 592. 593. 594. 596. 598. 599. 602. 603. 609. 610. 616. 634. 655. 674. 689. 729. nan] D4: float64, 748, %29.41 [-122. -90. -83. -42. -15. -6. -2. -1. 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265. 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279. 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293. 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307. 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321. 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335. 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349. 350. 351. 352. 353. 354. 355. 356. 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334. 335. 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349. 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363. 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377. 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391. 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405. 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419. 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433. 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447. 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461. 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475. 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489. 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503. 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517. 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531. 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545. 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559. 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573. 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587. 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601. 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615. 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629. 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. 641. 642. 643. 644. 645. 646. 647. 648. 649. 650. 651. 652. 653. 654. 655. 656. 657. 658. 659. 660. 661. 662. 663. 664. 665. 666. 667. 668. 669. 670. 671. 672. 674. 677. 678. 680. 681. 683. 685. 687. 689. 690. 691. 692. 693. 694. 696. 700. 705. 707. 711. 713. 714. 715. 717. 718. 721. 722. 723. 724. 725. 726. 727. 728. 729. 730. 731. 732. 733. 734. 735. 736. 737. 738. 739. 740. 742. 744. 745. 746. 747. 748. 750. 751. 752. 753. 754. 755. 756. 758. 760. 761. 763. 764. 768. 769. 770. 772. 773. 774. 779. 784. 786. 789. 790. 791. 796. 798. 800. 801. 804. 807. 809. 810. 811. 812. 813. 814. 815. 818. 819. 820. 823. 824. 825. 827. 829. 830. 833. 834. 835. 836. 837. 838. 839. 840. 841. 842. 843. 844. 845. 847. 849. 850. 851. 854. 855. 856. 857. 859. 861. 864. 865. 867. 868. 876. 878. 879. nan]
columns=[col for col in train.columns if re.search('^D\d.*', col)]
corr_treshold = 0.75
drop_col = remove_collinear_features(train[columns],corr_treshold)
drop_col
{'D11', 'D12', 'D2', 'D4'}
# Create a correlation heatmap using the make_corr function
make_corr(columns, train)
# The correlated columns having the most missing values are dropped. So, we replaced some columns in the dropping column list below.
drop_col={'D11', 'D12', 'D2', 'D4'}
for df in [train, test]:
df = df.drop(drop_col, axis=1)
column_details(regex='^M\d*', df=train)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE M1: object, 2, %51.88 ['F' 'T' nan] M2: object, 2, %51.88 ['F' 'T' nan] M3: object, 2, %51.88 ['F' 'T' nan] M4: object, 3, %48.26 ['M0' 'M1' 'M2' nan] M5: object, 2, %60.23 ['F' 'T' nan] M6: object, 2, %30.45 ['F' 'T' nan] M7: object, 2, %64.81 ['F' 'T' nan] M8: object, 2, %64.8 ['F' 'T' nan] M9: object, 2, %64.8 ['F' 'T' nan]
# Just M4 has different categories, others have F,T,nan
fig, ax = plt.subplots(3, 3, figsize=(20, 15))
fig.suptitle('Count Plots for Columns M1 to M9 in Train Data', fontsize=16)
for i, col in enumerate(['M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9']):
row_num = i // 3
col_num = i % 3
sns.countplot(x=col, ax=ax[row_num, col_num], hue='isFraud', data=train)
ax[row_num, col_num].set_title(f'{col} Train', fontsize=14)
# Target Encoding for M columns
# Define the list of columns from 'M1' to 'M9'
m_columns = [f'M{i}' for i in range(1, 10)]
# Calculate and replace target mean for each column
for col in m_columns:
temp_dict = train.groupby([col])['isFraud'].agg(['mean']).to_dict()['mean']
train[col+'_target_encoded'] = train[col].replace(temp_dict)
test[col+'_target_encoded'] = test[col].replace(temp_dict)
# List of original columns to drop
columns_to_drop = ['M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9']
# Drop the original columns
train.drop(columns_to_drop, axis=1, inplace=True)
test.drop(columns_to_drop, axis=1, inplace=True)
We identified redundancy and correlation among the 'V' columns, and dropped correlated columns with a correlation coefficient (r) greater than 0.75. This process resulted in retaining only 69 independent 'V' columns.
column_details(regex='V\d*', df=df)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE V1: float32, 2, %30.12 [ 0. 1. nan] V2: float32, 9, %30.12 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. nan] V3: float32, 10, %30.12 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. nan] V4: float32, 7, %30.12 [ 0. 1. 2. 3. 4. 5. 6. nan] V5: float32, 7, %30.12 [ 0. 1. 2. 3. 4. 5. 6. nan] V6: float32, 10, %30.12 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. nan] V7: float32, 10, %30.12 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. nan] V8: float32, 9, %30.12 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. nan] V9: float32, 9, %30.12 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. nan] V10: float32, 5, %30.12 [ 0. 1. 2. 3. 4. nan] V11: float32, 6, %30.12 [ 0. 1. 2. 3. 4. 5. nan] V12: float32, 4, %8.45 [ 0. 1. 2. 3. nan] V13: float32, 7, %8.45 [ 0. 1. 2. 3. 4. 5. 6. nan] V14: float32, 2, %8.45 [ 0. 1. nan] V15: float32, 3, %8.45 [ 0. 1. 2. nan] V16: float32, 5, %8.45 [ 0. 1. 2. 3. 4. nan] V17: float32, 10, %8.45 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. nan] V18: float32, 10, %8.45 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. nan] V19: float32, 5, %8.45 [ 0. 1. 2. 3. 4. nan] V20: float32, 7, %8.45 [ 0. 1. 2. 3. 4. 5. 6. nan] V21: float32, 4, %8.45 [ 0. 1. 2. 3. nan] V22: float32, 6, %8.45 [ 0. 1. 2. 3. 4. 7. nan] V23: float32, 9, %8.45 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. nan] V24: float32, 9, %8.45 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. nan] V25: float32, 5, %8.45 [ 0. 1. 2. 3. 4. nan] V26: float32, 5, %8.45 [ 0. 1. 2. 3. 4. nan] V27: float32, 2, %8.45 [ 0. 1. nan] V28: float32, 3, %8.45 [ 0. 1. 2. nan] V29: float32, 5, %8.45 [ 0. 1. 2. 3. 4. nan] V30: float32, 7, %8.45 [ 0. 1. 2. 3. 4. 5. 6. nan] V31: float32, 4, %8.45 [ 0. 1. 2. 3. nan] V32: float32, 5, %8.45 [ 0. 1. 2. 3. 4. nan] V33: float32, 3, %8.45 [ 0. 1. 2. nan] V34: float32, 5, %8.45 [ 0. 1. 2. 3. 4. nan] V35: float32, 4, %26.21 [ 0. 1. 2. 3. nan] V36: float32, 6, %26.21 [ 0. 1. 2. 3. 4. 5. nan] V37: float32, 18, %26.21 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 18. 21. nan] V38: float32, 31, %26.21 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. nan] V39: float32, 16, %26.21 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. nan] V40: float32, 17, %26.21 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 24. nan] V41: float32, 2, %26.21 [ 0. 1. nan] V42: float32, 6, %26.21 [ 0. 1. 2. 3. 4. 5. nan] V43: float32, 8, %26.21 [ 0. 1. 2. 3. 4. 5. 6. 7. nan] V44: float32, 17, %26.21 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 15. 19. 32. nan] V45: float32, 22, %26.21 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 32. nan] V46: float32, 6, %26.21 [ 0. 1. 2. 3. 4. 5. nan] V47: float32, 7, %26.21 [ 0. 1. 2. 3. 4. 5. 6. nan] V48: float32, 4, %26.21 [ 0. 1. 2. 3. nan] V49: float32, 6, %26.21 [ 0. 1. 2. 3. 4. 5. nan] V50: float32, 5, %26.21 [ 0. 1. 2. 3. 4. nan] V51: float32, 6, %26.21 [ 0. 1. 2. 3. 4. 5. nan] V52: float32, 7, %26.21 [ 0. 1. 2. 3. 4. 5. 6. nan] V53: float32, 5, %8.46 [ 0. 1. 2. 3. 4. nan] V54: float32, 5, %8.46 [ 0. 1. 2. 3. 4. nan] V55: float32, 14, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. nan] V56: float32, 52, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. nan] V57: float32, 7, %8.46 [ 0. 1. 2. 3. 4. 5. 6. nan] V58: float32, 11, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. nan] V59: float32, 17, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. nan] V60: float32, 17, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. nan] V61: float32, 7, %8.46 [ 0. 1. 2. 3. 4. 5. 6. nan] V62: float32, 11, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. nan] V63: float32, 6, %8.46 [ 0. 1. 2. 3. 4. 5. nan] V64: float32, 8, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. nan] V65: float32, 2, %8.46 [ 0. 1. nan] V66: float32, 8, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. nan] V67: float32, 8, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. nan] V68: float32, 3, %8.46 [ 0. 1. 2. nan] V69: float32, 5, %8.46 [ 0. 1. 2. 3. 4. nan] V70: float32, 6, %8.46 [ 0. 1. 2. 3. 4. 5. nan] V71: float32, 7, %8.46 [ 0. 1. 2. 3. 4. 5. 6. nan] V72: float32, 11, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. nan] V73: float32, 8, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. nan] V74: float32, 8, %8.46 [ 0. 1. 2. 3. 4. 5. 6. 7. nan] V75: float32, 4, %11.4 [ 0. 1. 2. 3. nan] V76: float32, 6, %11.4 [ 0. 1. 2. 3. 4. 5. nan] V77: float32, 19, %11.4 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. nan] V78: float32, 32, %11.4 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. nan] V79: float32, 8, %11.4 [ 0. 1. 2. 3. 4. 5. 6. 7. nan] V80: float32, 16, %11.4 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. nan] V81: float32, 16, %11.4 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. nan] V82: float32, 7, %11.4 [ 0. 1. 2. 3. 4. 5. 6. nan] V83: float32, 8, %11.4 [ 0. 1. 2. 3. 4. 5. 6. 7. nan] V84: float32, 7, %11.4 [ 0. 1. 2. 3. 4. 5. 7. nan] V85: float32, 8, %11.4 [ 0. 1. 2. 3. 4. 5. 6. 7. nan] V86: float32, 14, %11.4 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. nan] V87: float32, 23, %11.4 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. nan] V88: float32, 2, %11.4 [ 0. 1. nan] V89: float32, 3, %11.4 [ 0. 1. 2. nan] V90: float32, 5, %11.4 [ 0. 1. 2. 3. 4. nan] V91: float32, 6, %11.4 [ 0. 1. 2. 3. 4. 5. nan] V92: float32, 7, %11.4 [ 0. 1. 2. 3. 4. 5. 6. nan] V93: float32, 8, %11.4 [ 0. 1. 2. 3. 4. 5. 6. 7. nan] V94: float32, 3, %11.4 [ 0. 1. 2. nan] V95: float32, 881, %0.21 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265. 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279. 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293. 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307. 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321. 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335. 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349. 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363. 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377. 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391. 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405. 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419. 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433. 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447. 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461. 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475. 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489. 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503. 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517. 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531. 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545. 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559. 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573. 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587. 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601. 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615. 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629. 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. 641. 642. 643. 644. 645. 646. 647. 648. 649. 650. 651. 652. 653. 654. 655. 656. 657. 658. 659. 660. 661. 662. 663. 664. 665. 666. 667. 668. 669. 670. 671. 672. 673. 674. 675. 676. 677. 678. 679. 680. 681. 682. 683. 684. 685. 686. 687. 688. 689. 690. 691. 692. 693. 694. 695. 696. 697. 698. 699. 700. 701. 702. 703. 704. 705. 706. 707. 708. 709. 710. 711. 712. 713. 714. 715. 716. 717. 718. 719. 720. 721. 722. 723. 724. 725. 726. 727. 728. 729. 730. 731. 732. 733. 734. 735. 736. 737. 738. 739. 740. 741. 742. 743. 744. 745. 746. 747. 748. 749. 750. 751. 752. 753. 754. 755. 756. 757. 758. 759. 760. 761. 762. 763. 764. 765. 766. 767. 768. 769. 770. 771. 772. 773. 774. 775. 776. 777. 778. 779. 780. 781. 782. 783. 784. 785. 786. 787. 788. 789. 790. 791. 792. 793. 794. 795. 796. 797. 798. 799. 800. 801. 802. 803. 804. 805. 806. 807. 808. 809. 810. 811. 812. 813. 814. 815. 816. 817. 818. 819. 820. 821. 822. 823. 824. 825. 826. 827. 828. 829. 830. 831. 832. 833. 834. 835. 836. 837. 838. 839. 840. 841. 842. 843. 844. 845. 846. 847. 848. 849. 850. 851. 852. 853. 854. 855. 856. 857. 858. 859. 860. 861. 862. 863. 864. 865. 866. 867. 868. 869. 870. 871. 872. 873. 874. 875. 876. 877. 878. 879. 880. nan] V96: float32, 1410, %0.21 [0.000e+00 1.000e+00 2.000e+00 ... 1.409e+03 1.410e+03 nan] V97: float32, 976, %0.21 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265. 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279. 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293. 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307. 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321. 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335. 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349. 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363. 364. 365. 366. 367. 368. 369. 370. 372. 373. 374. 375. 376. 377. 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391. 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405. 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419. 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433. 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447. 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461. 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475. 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489. 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503. 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517. 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531. 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545. 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559. 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573. 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587. 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601. 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 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814. 815. 816. 817. 818. 819. 820. 821. 822. 823. 824. 825. 826. 827. 828. 829. 830. 831. 832. 833. 834. 835. 836. 837. 838. 839. 840. 841. 842. 843. 844. 845. 846. 847. 848. 849. 850. 851. 852. 853. 854. 855. 856. 857. 858. 859. 860. 861. 862. 863. 864. 865. 866. 867. 868. 869. 870. 871. 872. 873. 874. 875. 876. 877. 878. 879. 880. 881. 882. 883. 884. 885. 886. 887. 888. 889. 890. 891. 892. 893. 894. 895. 896. 897. 898. 899. 900. 901. 902. 903. 904. 905. 906. 907. 908. 909. 910. 911. 912. 913. 914. 915. 916. 917. 918. 919. 920. 921. 922. 923. 924. 925. 926. 927. 928. 929. 930. 931. 932. 933. 934. 935. 936. 937. 938. 939. 940. 941. 942. 943. 944. 945. 946. 947. 948. 949. 950. 951. 952. 953. 954. 955. 956. 957. 958. 959. 960. 961. 962. 963. 964. 965. 966. 967. 968. 969. 970. 971. 972. 973. 974. 975. 976. nan] V99: float32, 64, %0.21 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. nan] V100: float32, 21, %0.21 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. nan] V126: float32, 4702, %0.21 [0.0000e+00 7.5470e-01 8.7820e-01 ... 9.4795e+04 9.4901e+04 nan] V127: float32, 11600, %0.21 [0.00000e+00 7.54700e-01 8.78200e-01 ... 1.46211e+05 1.46252e+05 nan] V128: float32, 6679, %0.21 [0.00000e+00 7.54700e-01 8.78200e-01 ... 1.03439e+05 1.03495e+05 nan] V130: float32, 6270, %0.21 [0.000e+00 1.000e+00 2.000e+00 ... 2.560e+04 3.025e+04 nan] V131: float32, 2164, %0.21 [0.00e+00 1.00e+00 2.00e+00 ... 9.60e+03 1.25e+04 nan] V138: float32, 9, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. nan] V139: float32, 25, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. nan] V140: float32, 28, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. nan] V141: float32, 4, %90.04 [ 0. 1. 2. 3. nan] V142: float32, 6, %90.04 [ 0. 1. 2. 3. 4. 5. nan] V143: float32, 870, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265. 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279. 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293. 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307. 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321. 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335. 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349. 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363. 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377. 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391. 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405. 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419. 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433. 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447. 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461. 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475. 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489. 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503. 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517. 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531. 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545. 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559. 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573. 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587. 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601. 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615. 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629. 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. 641. 642. 643. 644. 645. 646. 647. 648. 649. 650. 651. 652. 653. 654. 655. 656. 657. 658. 659. 660. 661. 662. 663. 664. 665. 666. 667. 668. 669. 670. 671. 672. 673. 674. 675. 676. 677. 678. 679. 680. 681. 682. 683. 684. 685. 686. 687. 688. 689. 690. 691. 692. 693. 694. 695. 696. 697. 698. 699. 700. 701. 702. 703. 704. 705. 706. 707. 708. 709. 710. 711. 712. 713. 714. 715. 716. 717. 718. 719. 720. 721. 722. 723. 724. 725. 726. 727. 728. 729. 730. 731. 732. 733. 734. 735. 736. 737. 738. 739. 740. 741. 742. 743. 744. 745. 746. 747. 748. 749. 750. 751. 752. 753. 754. 755. 756. 757. 758. 759. 760. 761. 762. 763. 764. 765. 766. 767. 768. 769. 770. 771. 772. 773. 774. 775. 776. 777. 778. 779. 780. 781. 782. 783. 784. 785. 786. 787. 788. 789. 790. 791. 792. 793. 794. 795. 796. 797. 798. 799. 800. 801. 802. 803. 804. 805. 806. 807. 808. 809. 810. 811. 812. 813. 814. 815. 816. 817. 818. 819. 820. 821. 822. 823. 824. 825. 826. 827. 828. 829. 830. 831. 832. 833. 834. 835. 836. 837. 838. 839. 840. 841. 842. 843. 844. 845. 846. 847. 848. 849. 850. 851. 852. 853. 854. 855. 856. 857. 858. 859. 860. 861. 862. 863. 864. 865. 866. 867. 868. 869. nan] V144: float32, 24, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. nan] V145: float32, 56, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. nan] V146: float32, 25, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. nan] V147: float32, 27, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. nan] V148: float32, 21, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. nan] V149: float32, 21, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. nan] V150: float32, 101, %90.04 [ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. nan] V151: float32, 22, %90.04 [ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. nan] V152: float32, 26, %90.04 [ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 52. 53. 54. 55. 56. 57. 58. 59. nan] V153: float32, 19, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. nan] V154: float32, 19, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. nan] V155: float32, 25, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. nan] V156: float32, 25, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. nan] V157: float32, 25, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. nan] V158: float32, 25, %90.04 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. nan] V159: float32, 514, %90.04 [0.00000e+00 5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 1.00000e+01 1.20000e+01 1.30000e+01 1.40000e+01 1.50000e+01 1.60000e+01 1.70000e+01 2.00000e+01 2.50000e+01 2.80000e+01 3.00000e+01 3.30000e+01 3.50000e+01 3.60000e+01 3.70000e+01 4.00000e+01 4.50000e+01 5.00000e+01 5.50000e+01 6.00000e+01 6.50000e+01 7.00000e+01 7.50000e+01 8.00000e+01 8.50000e+01 1.00000e+02 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53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. nan] V181: float32, 25, %80.85 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. nan] V182: float32, 84, %80.85 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. nan] V183: float32, 42, %80.85 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. nan] V184: float32, 13, %80.77 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. nan] V185: float32, 32, %80.77 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 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1.100000e+03 1.150000e+03 1.175000e+03 1.200000e+03 1.225000e+03 1.275000e+03 1.300000e+03 1.325000e+03 1.350000e+03 1.375000e+03 1.400000e+03 1.425000e+03 1.450000e+03 1.475000e+03 1.500000e+03 1.525000e+03 1.575000e+03 1.600000e+03 1.625000e+03 1.650000e+03 1.700000e+03 1.725000e+03 1.775000e+03 1.800000e+03 1.825000e+03 1.875000e+03 1.900000e+03 1.925000e+03 1.950000e+03 2.000000e+03 2.025000e+03 2.050000e+03 2.100000e+03 2.125000e+03 2.150000e+03 2.275000e+03 2.300000e+03 2.375000e+03 2.400000e+03 2.500000e+03 2.525000e+03 2.700000e+03 2.800000e+03 3.600000e+03 4.250000e+03 5.400000e+03 7.200000e+03 8.000000e+03 9.000000e+03 9.600000e+03 1.250000e+04 nan] V208: float32, 783, %80.77 [ 0. 1.7384 2.1078 2.4592 2.5981 2.6807 2.7051 3.0125 3.2627 3.2754 3.3146 3.4063 3.5828 3.8732 3.9347 4.0746 4.1806 4.1976 4.5495 4.6725 4.7509 4.7954 4.8336 4.8569 5. 5.0414 5.2873 5.3048 5.3488 5.4717 5.8406 5.8427 6.042 6.0865 6.2095 6.3324 6.36 6.3939 6.4554 6.5169 6.5254 6.6398 6.6462 6.7066 6.7628 6.8863 7. 7.0087 7.1317 7.1932 7.3161 7.3718 7.3776 7.5011 7.5525 7.6235 7.6405 7.6574 7.685 7.7465 7.8694 7.9754 7.9929 8. 8.0539 8.1154 8.2383 8.2775 8.2998 8.3612 8.3613 8.6072 8.6687 8.7514 8.9146 8.9761 9.0376 9.099 9.1605 9.1775 9.222 9.2835 9.3004 9.345 9.4679 9.5294 9.5464 9.5909 9.6524 9.6672 9.7138 9.7753 9.8368 9.8485 10. 10.0827 10.1442 10.2057 10.2248 10.2714 10.5131 10.5746 10.6 10.6975 10.8205 10.9434 11.0049 11.0219 11.0664 11.1279 11.1894 11.2508 11.2985 11.3738 11.4353 11.5582 11.6812 11.7427 11.8423 11.8656 12.0501 12.173 12.2048 12.3575 12.5419 12.6034 12.6649 12.7264 12.7486 12.9723 13.0338 13.0507 13.0952 13.2182 13.2797 13.3412 13.4026 13.4641 13.5256 13.5341 13.5871 13.5945 13.6358 13.6486 13.71 13.7721 13.8902 13.895 14.0174 14.2019 14.2634 14.3863 14.4483 14.5093 14.6322 14.6492 14.6937 14.7197 14.743 14.7552 14.8029 14.8167 14.8321 14.8787 14.9396 15. 15.0016 15.105 15.1055 15.2258 15.247 15.37 15.432 15.4681 15.5279 15.5544 15.6159 15.6774 15.7389 15.8618 15.9 15.9233 15.9509 15.9853 16.1078 16.2535 16.3134 16.3706 16.4766 16.5996 16.6759 16.7226 16.801 16.8572 16.8921 16.977 17.0914 17.1529 17.153 17.2144 17.2759 17.2807 17.3374 17.4603 17.5218 17.7031 17.7062 17.8292 17.9522 17.9829 18. 18.1981 18.321 18.3825 18.444 18.567 18.6094 18.6899 18.8128 18.8744 18.9358 19.0588 19.1203 19.1818 19.2432 19.3047 19.3344 19.3662 19.4277 19.6121 19.6365 19.6736 19.7966 19.9195 20. 20.0425 20.1199 20.1654 20.2884 20.4114 20.4728 20.5343 20.5958 20.6573 20.6636 20.7802 20.7845 20.8417 20.899 20.9032 20.9647 21.0262 21.0569 21.1237 21.2106 21.2678 21.2721 21.395 21.4565 21.518 21.641 21.7024 21.7639 21.8869 22.0098 22.1328 22.1943 22.3172 22.3787 22.4402 22.5017 22.5632 22.6246 22.6861 22.7179 22.7476 22.8706 22.9935 23.0804 23.1165 23.2394 23.3624 23.4854 23.5469 23.6083 23.7928 23.8209 23.8542 24.0387 24.0472 24.1002 24.168 24.2231 24.3461 24.4076 24.469 24.5305 24.592 24.715 24.8379 24.8994 24.9609 25. 25.0838 25.2068 25.2683 25.3764 25.3912 25.4527 25.5142 25.5757 25.6785 25.6986 25.7389 25.7601 25.8216 25.8428 25.8831 25.9446 25.9806 26.0675 26.129 26.1905 26.252 26.4051 26.4364 26.464 26.5594 26.5848 26.6823 26.8053 26.9282 27.0512 27.1127 27.189 27.1916 27.2494 27.2717 27.2971 27.4816 27.666 27.6724 27.7895 27.7932 27.8504 27.9119 28.0348 28.0349 28.1578 28.2199 28.2808 28.3974 28.4658 28.5882 28.8961 29.0021 29.0026 29.1415 29.2645 29.3265 29.4489 29.5104 29.5274 29.5719 29.6122 29.7563 29.7569 29.7648 29.8178 29.8793 30. 30.0028 30.0032 30.1252 30.1496 30.1867 30.21 30.2651 30.3096 30.3711 30.4941 30.5556 30.5725 30.6176 30.74 30.8142 30.8635 30.9244 30.9859 31.0474 31.1089 31.1799 31.2318 31.2371 31.3548 31.4163 31.4184 31.4778 31.6007 31.6601 31.8466 31.9018 31.9701 32.0311 32.1434 32.1917 32.2155 32.2643 32.3385 32.3894 32.4614 32.5844 32.7476 32.8308 32.9289 33. 33.0148 33.1102 33.1992 33.2607 33.5071 33.5336 33.5395 33.6916 33.814 33.8755 33.9375 34.0599 34.1829 34.3058 34.4288 34.5518 34.6133 34.6747 34.7977 34.8592 34.9206 35. 35.228 35.2896 35.351 35.4125 35.4496 35.5354 35.6584 35.7082 35.9658 36.0273 36.2732 36.3347 36.5191 36.5806 36.6145 36.6421 36.7036 36.765 36.888 36.977 37.011 37.2442 37.3798 37.4413 37.5643 38.0561 38.1176 38.1791 38.3635 38.7324 38.7939 38.8512 39.1522 39.2242 39.2857 39.3472 39.4087 39.5316 39.5931 39.7161 39.839 39.962 40. 40.085 40.2079 40.2694 40.3309 40.4538 40.5768 40.6022 40.6998 40.7612 40.8227 40.9075 40.9456 41.0072 41.3146 41.376 41.569 41.6834 42.0523 42.1138 42.2368 42.4212 42.6671 42.7774 42.913 43. 43.036 43.159 43.2586 43.5278 43.6508 43.8967 44.0812 44.1119 44.1426 44.2042 44.2656 44.4691 44.5115 44.6345 44.8189 44.8762 45. 45.0034 45.2493 45.4337 45.4952 45.6171 45.6182 45.6775 45.6796 45.7411 46. 46.11 46.233 46.2817 46.4174 46.4788 46.6018 46.7248 46.7651 46.9707 47.1552 47.4011 47.7085 47.8929 48.2003 48.4462 48.4568 48.6922 49.1501 49.184 49.4299 49.6144 50. 50.1677 50.4136 50.5366 50.721 50.9054 51.3973 51.4588 51.6432 52.0121 52.0736 52.135 52.258 52.9343 53.1187 53.4876 54.1363 54.2254 54.7177 55. 55.0866 55.103 55.332 55.394 55.5164 56.1927 56.2547 56.3114 56.3157 56.4392 56.6846 57.3608 57.3932 57.4223 57.5453 57.6682 57.6688 57.7917 58.283 58.7283 59.0208 59.5132 59.6356 60. 60.066 60.4348 60.6193 60.7422 60.8037 60.9548 61.1726 61.357 61.603 61.7264 62.2474 62.4637 62.495 62.6481 62.8326 63.079 63.2014 63.4473 63.5703 63.9397 64.431 64.7384 65. 65.0458 65.0628 65.1688 65.5059 65.6612 65.9792 66.3984 67.0137 67.0667 67.505 67.8744 67.9969 68.1198 68.6117 68.6732 68.9806 69.227 69.4724 70. 70.1487 70.4561 70.702 70.9479 71.0708 71.0709 71.1938 71.9316 73.1612 73.229 73.2842 73.5306 73.776 73.8767 73.9329 74.8 75. 75.2515 75.4042 76.1737 76.4202 76.6041 77.1574 77.3418 78.0796 78.5714 78.5958 78.8174 79.0633 79.1502 79.7544 79.8625 80. 80.1699 80.4794 80.8419 80.9077 81.1536 81.2766 81.6454 82.2475 82.8326 84.4608 84.4735 84.5965 85. 85.0883 85.5802 85.8261 86.3179 86.5024 86.5638 87.0557 87.1256 87.768 88.1623 88.2238 88.3775 89.6378 89.7608 90.1127 90.4986 92.22 92.8348 94.0644 94.8022 95. 96.6466 100. 100.8277 100.9501 103.2864 104.393 104.516 104.7683 104.7689 105. 105.2845 107.713 108.4507 108.5737 110. 110.2336 110.664 115.152 115.8283 117.7538 118.2875 120. 120.2549 122.2222 124.1896 124.3126 125. 125.911 126.8333 127.3866 130.2146 130.8294 131.5672 133.719 139.2522 139.4366 139.5596 140.7277 141.1581 141.404 142.0803 142.3877 144.9698 148.6586 150. 153.3926 153.9655 156.9584 159.4473 170. 170.2636 173.8654 183.3747 185.6696 189.7273 192.0148 200. 212.106 212.4749 221.5745 225. 230.1811 250. 251.0848 256.6175 300. 325. 389.7832 400. 434.8889 475. 500. 600. 675. 700. 725. nan] V209: float32, 1088, %80.77 [0.0000e+00 1.7384e+00 2.1078e+00 ... 2.4750e+03 2.6000e+03 nan] V210: float32, 880, %80.77 [ 0. 1.7384 2.1078 2.4592 2.6807 2.7051 3.0125 3.2627 3.2754 3.3146 3.4063 3.5828 3.8732 3.9347 4.0746 4.1806 4.1976 4.5495 4.6725 4.7509 4.7954 4.8336 4.8569 5. 5.0414 5.2873 5.3048 5.3488 5.4717 5.8406 5.8427 6.042 6.0865 6.2095 6.2254 6.3324 6.36 6.3939 6.4554 6.5169 6.5254 6.6398 6.6462 6.7066 6.7628 6.8863 7. 7.0087 7.1317 7.1932 7.3161 7.3718 7.3776 7.5011 7.5525 7.6235 7.6405 7.6574 7.685 7.7465 7.8694 7.9754 7.9929 8. 8.0539 8.1154 8.2383 8.2775 8.2998 8.3612 8.3613 8.6072 8.6687 8.7514 8.9146 8.9761 9.0376 9.099 9.1605 9.1775 9.222 9.2835 9.3004 9.345 9.4679 9.5294 9.5464 9.5909 9.6524 9.6672 9.7138 9.7753 9.8368 9.8485 10. 10.0827 10.1442 10.2057 10.2248 10.2714 10.5131 10.5746 10.5921 10.6 10.6975 10.8205 10.9434 11.0049 11.0219 11.0664 11.1279 11.1894 11.2508 11.2985 11.3738 11.4353 11.5582 11.6812 11.7427 11.8041 11.8656 12. 12.0501 12.173 12.2048 12.2345 12.3575 12.5419 12.6034 12.6649 12.7264 12.7486 12.8218 12.9723 13.0338 13.0507 13.0952 13.2182 13.2797 13.3412 13.4026 13.4641 13.5256 13.5871 13.5945 13.6358 13.6486 13.71 13.7721 13.7726 13.8902 13.895 14.0174 14.2019 14.2634 14.3863 14.4483 14.5093 14.6322 14.6492 14.6937 14.7197 14.7552 14.8167 14.8321 14.8787 14.9396 15. 15.0011 15.0016 15.105 15.1055 15.247 15.37 15.432 15.4681 15.5279 15.5544 15.6159 15.6774 15.7092 15.7389 15.8618 15.9 15.9233 15.9509 15.9853 16.1078 16.3134 16.3706 16.4766 16.5996 16.6759 16.7226 16.801 16.8572 16.8921 16.977 17.0914 17.1529 17.153 17.2144 17.2759 17.2807 17.3374 17.4603 17.5218 17.7031 17.7062 17.8292 17.9522 17.9829 18. 18.1981 18.321 18.3825 18.444 18.567 18.6094 18.6899 18.8128 18.8744 18.9358 19.0588 19.1203 19.1818 19.2432 19.3047 19.3662 19.4277 19.538 19.6121 19.6365 19.6736 19.7966 19.9195 20. 20.0425 20.1654 20.2884 20.4114 20.4728 20.5343 20.5958 20.6573 20.6636 20.7802 20.7845 20.8417 20.899 20.9032 20.9647 21. 21.0262 21.0569 21.1237 21.1492 21.2106 21.2117 21.2678 21.2721 21.395 21.4565 21.518 21.641 21.7024 21.7639 21.8254 21.8869 22.0098 22.029 22.1328 22.1943 22.3172 22.3787 22.4402 22.5017 22.5632 22.5801 22.6246 22.6861 22.7179 22.7476 22.8706 22.9935 23.0804 23.1165 23.2394 23.2395 23.3624 23.4854 23.5469 23.6083 23.7928 23.8209 23.8542 24.0387 24.0472 24.1002 24.168 24.2231 24.2846 24.3461 24.4076 24.469 24.5305 24.592 24.715 24.8379 24.8994 24.9609 25. 25.0838 25.1453 25.2068 25.2683 25.3764 25.3912 25.4527 25.5142 25.5757 25.6986 25.7601 25.8216 25.8428 25.8831 25.9446 25.9806 26. 26.0675 26.129 26.1905 26.252 26.4051 26.4364 26.464 26.5594 26.5848 26.6823 26.8053 26.9282 27.0512 27.1127 27.1916 27.2717 27.2971 27.3062 27.4816 27.543 27.666 27.6724 27.7895 27.7932 27.8504 27.9119 28.0348 28.0349 28.1578 28.2199 28.2766 28.2808 28.3974 28.4658 28.5882 28.8961 29.0021 29.0026 29.1415 29.2645 29.3265 29.4489 29.5104 29.5274 29.5719 29.6122 29.7563 29.7569 29.7648 29.8178 29.8793 30. 30.0028 30.0032 30.1252 30.1496 30.1867 30.21 30.2651 30.3096 30.3097 30.3711 30.4516 30.4517 30.4941 30.5556 30.5725 30.6176 30.74 30.8142 30.8635 30.9244 30.9859 31.0474 31.1089 31.1799 31.2318 31.2371 31.2933 31.3548 31.4163 31.4184 31.4778 31.6007 31.6601 31.8466 31.9018 31.9701 32.0311 32.1434 32.1917 32.2155 32.2643 32.3385 32.3894 32.4614 32.5844 32.7074 32.7476 32.7863 32.8308 32.9289 33. 33.0148 33.1102 33.1992 33.2607 33.5071 33.5331 33.5395 33.6916 33.814 33.8755 33.9375 34.0599 34.1829 34.3058 34.4288 34.5518 34.6132 34.6133 34.6747 34.7977 34.8592 34.9206 35. 35.228 35.2896 35.351 35.4125 35.4496 35.5354 35.6584 35.7082 35.9658 36. 36.0273 36.2732 36.3347 36.4576 36.5191 36.5806 36.6145 36.6421 36.7036 36.765 36.888 36.977 37.011 37.2442 37.3798 37.4413 37.5643 38.0561 38.1176 38.1791 38.302 38.3635 38.4865 38.7324 38.7939 39.2242 39.2857 39.326 39.3472 39.4087 39.5316 39.5931 39.7161 39.839 39.962 40. 40.085 40.2079 40.2694 40.3309 40.4538 40.5768 40.6022 40.6998 40.7612 40.8227 40.9075 40.9456 41.0072 41.3146 41.376 41.569 41.6834 42.0523 42.1138 42.2368 42.2982 42.4212 42.6671 42.7774 42.913 43.036 43.159 43.2586 43.5278 43.6508 43.8967 44.0812 44.1119 44.1426 44.2042 44.2656 44.2884 44.4691 44.5115 44.6345 44.8189 44.8762 45. 45.0034 45.2493 45.4337 45.4952 45.6171 45.6182 45.6775 45.6796 45.7411 46. 46.11 46.1715 46.233 46.2817 46.4174 46.4788 46.6018 46.6442 46.7248 46.8864 46.9707 47.1552 47.2166 47.4011 47.4625 47.5861 47.7085 47.8929 48. 48.2003 48.3233 48.4462 48.4568 48.6922 48.9402 49.1501 49.184 49.3028 49.4299 49.6144 50. 50.1677 50.4136 50.5366 50.721 50.9054 51.357 51.3973 51.4588 51.6432 52.0121 52.0318 52.0736 52.135 52.258 52.9343 53.1187 53.4097 53.4876 53.795 54.1363 54.2254 54.378 54.7177 55. 55.0866 55.103 55.332 55.394 55.5164 55.6394 56.1927 56.2547 56.3114 56.3157 56.4392 56.5531 56.6846 57.3608 57.3932 57.4223 57.5453 57.6682 57.6688 57.7917 57.8527 58.283 58.7282 58.7283 59.0208 59.3897 59.5132 59.6356 60. 60.066 60.4348 60.6193 60.7422 60.8037 60.9548 61.1726 61.357 61.603 61.7264 62.0333 62.2474 62.4637 62.495 62.5358 62.6481 62.8326 63. 63.079 63.2014 63.4473 63.5703 63.8565 63.9397 64.431 64.7384 65. 65.0458 65.0628 65.1688 65.2536 65.5059 65.6612 65.9681 65.9792 66.2755 66.3984 66.6443 67.0137 67.0667 67.0672 67.1362 67.505 67.5055 67.8744 67.9969 68.1198 68.4802 68.6117 68.6732 68.9806 69.227 69.4724 70. 70.1487 70.4561 70.702 70.9479 71.0708 71.0709 71.1938 71.9316 73. 73.1612 73.229 73.2842 73.5306 73.776 73.8767 73.9329 74.5137 74.8826 75. 75.2515 75.4042 76.1737 76.2352 76.4202 76.6041 76.727 77.1574 77.3418 78.0796 78.5714 78.5958 78.8174 79.0633 79.1502 79.1863 79.7544 79.8625 80. 80.1699 80.4794 80.8419 80.9077 81.1536 81.2766 81.5225 81.6274 81.6454 82.2475 82.8326 84.4608 84.4735 84.5965 85. 85.0883 85.5759 85.5802 85.8261 86.3179 86.5024 86.5638 87.0557 87.1256 87.768 88.1623 88.2238 88.3775 89.6378 89.7608 89.8319 90. 90.1127 92.22 92.8348 94.0644 94.1874 94.8022 95. 96.4006 96.6466 100. 100.8277 100.9501 103.226 103.2864 104.393 104.516 104.7683 104.7689 105. 105.2845 106.115 107.713 108.4507 108.5737 110. 110.2336 110.664 113.3076 114.1938 115. 115.152 115.8283 117.7538 118.2875 120. 120.2549 122.2222 124.1896 124.3126 125. 125.911 126.2371 126.8333 127.3866 127.694 128.3151 128.4937 130. 130.2146 130.4606 130.8294 131.5672 131.8132 133.5346 133.719 135.01 139.2522 139.4366 139.5596 140. 140.7277 141.1581 141.404 142.0803 142.3877 144.9698 145. 145.0928 148.6586 150. 153.3926 153.9655 156.9584 159.4473 160. 165.3812 170. 170.2636 173.8654 176.755 180. 183.3747 185.6696 189.4198 189.7273 190. 192.0148 195. 200. 208.2942 212.106 212.4749 215.5182 221.5745 225. 230.1811 240. 250. 251.0848 256.6175 260. 270. 275. 300. 325. 335. 350. 389.7832 390. 400. 434.8889 465. 475. 500. 600. 675. 700. 725. nan] V211: float32, 3017, %80.85 [0.0000e+00 8.7820e-01 1.0006e+00 ... 9.2782e+04 9.2888e+04 nan] V212: float32, 3377, %80.85 [0.00000e+00 8.78200e-01 1.00060e+00 ... 1.28950e+05 1.29006e+05 nan] V213: float32, 3208, %80.85 [0.0000e+00 8.7820e-01 1.0006e+00 ... 9.7572e+04 9.7628e+04 nan] V214: float32, 732, %80.85 [0.000000e+00 1.308600e+00 2.151800e+00 2.291700e+00 2.336200e+00 2.582200e+00 3.012500e+00 3.091000e+00 3.582800e+00 3.811800e+00 3.890700e+00 3.934700e+00 4.197600e+00 4.488000e+00 4.611000e+00 4.795400e+00 5.000000e+00 5.348800e+00 5.471700e+00 5.656200e+00 6.000000e+00 6.393900e+00 6.516900e+00 6.578400e+00 6.762800e+00 7.000000e+00 7.131700e+00 7.165600e+00 7.193200e+00 7.377600e+00 7.412600e+00 7.623500e+00 7.685000e+00 7.992900e+00 8.000000e+00 8.053900e+00 8.299800e+00 8.344300e+00 8.361300e+00 8.484800e+00 8.607200e+00 8.668700e+00 8.730200e+00 8.791600e+00 8.808600e+00 8.914600e+00 8.993000e+00 9.000000e+00 9.099000e+00 9.153100e+00 9.283500e+00 9.467900e+00 9.529400e+00 9.590900e+00 9.775300e+00 9.898300e+00 1.000000e+01 1.014420e+01 1.045160e+01 1.069760e+01 1.088200e+01 1.100000e+01 1.120630e+01 1.143530e+01 1.161970e+01 1.168120e+01 1.200000e+01 1.217300e+01 1.229600e+01 1.235750e+01 1.241900e+01 1.248040e+01 1.266490e+01 1.278780e+01 1.291080e+01 1.315670e+01 1.315680e+01 1.321820e+01 1.327970e+01 1.352560e+01 1.358710e+01 1.364860e+01 1.371000e+01 1.377210e+01 1.383300e+01 1.389500e+01 1.400000e+01 1.401740e+01 1.426340e+01 1.432480e+01 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2.200980e+01 2.207130e+01 2.213280e+01 2.213540e+01 2.231720e+01 2.250170e+01 2.274760e+01 2.287060e+01 2.311650e+01 2.323940e+01 2.336240e+01 2.360830e+01 2.373130e+01 2.397870e+01 2.422310e+01 2.428460e+01 2.446900e+01 2.471500e+01 2.477640e+01 2.483790e+01 2.496080e+01 2.496090e+01 2.500000e+01 2.508380e+01 2.508390e+01 2.545270e+01 2.551420e+01 2.582160e+01 2.588310e+01 2.594460e+01 2.600000e+01 2.606750e+01 2.612900e+01 2.619050e+01 2.649790e+01 2.655930e+01 2.655940e+01 2.657690e+01 2.680530e+01 2.705120e+01 2.719160e+01 2.729720e+01 2.730350e+01 2.742010e+01 2.766600e+01 2.772750e+01 2.791190e+01 2.815780e+01 2.828080e+01 2.858820e+01 2.877260e+01 2.883410e+01 2.889610e+01 2.897560e+01 2.926450e+01 2.932650e+01 2.951040e+01 2.975690e+01 2.987930e+01 3.000000e+01 3.001970e+01 3.024820e+01 3.026510e+01 3.037110e+01 3.049400e+01 3.049410e+01 3.061760e+01 3.086400e+01 3.098600e+01 3.100000e+01 3.123180e+01 3.135480e+01 3.147780e+01 3.160070e+01 3.172360e+01 3.172370e+01 3.184660e+01 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31. 33. 34. 45. 46. 51. 54. 58. 59. 60. 67. 84. nan] V222: float32, 52, %80.73 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 44. 45. 46. 54. 61. 67. 68. 69. 70. 86. nan] V223: float32, 6, %82.95 [ 0. 1. 2. 3. 4. 5. nan] V224: float32, 29, %82.95 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 27. 36. 41. 52. nan] V225: float32, 14, %82.95 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 15. 19. nan] V226: float32, 78, %82.95 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 121. 242. nan] V227: float32, 20, %80.73 [ 0. 1. 2. 3. 4. 5. 6. 9. 13. 20. 27. 31. 32. 39. 42. 43. 50. 56. 60. 79. nan] V228: 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19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 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112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265. 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279. 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293. 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307. 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321. 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. nan] V234: float32, 96, %80.73 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. nan] V235: float32, 24, %82.95 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. nan] V236: float32, 46, %82.95 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. nan] V237: float32, 40, %82.95 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 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13.4026 13.4132 13.4641 13.5256 13.5457 13.5961 13.6358 13.833 13.8902 14.0174 14.0779 14.1383 14.1404 14.2019 14.2634 14.3248 14.3863 14.4483 14.5093 14.6216 14.6322 14.7552 14.8787 15. 15.0016 15.0181 15.1241 15.247 15.493 15.6159 15.6774 15.7092 15.7389 15.8618 15.8905 15.9 15.9853 16.1078 16.2307 16.2922 16.3537 16.3706 16.4766 16.5551 16.7226 16.784 16.801 16.8667 16.8921 16.969 17.0914 17.1529 17.153 17.2144 17.3374 17.3379 17.4603 17.5 17.5218 17.5833 17.6448 17.7062 17.8292 17.9522 18.1981 18.321 18.444 18.567 18.8128 18.8129 18.9358 19.0588 19.1203 19.2432 19.274 19.4277 19.5506 19.6121 19.7966 19.9195 19.999 20. 20.0425 20.0594 20.1654 20.2884 20.3499 20.3933 20.4114 20.4495 20.4728 20.5343 20.5958 20.6573 20.7188 20.8417 20.9032 21.0569 21.1237 21.1491 21.2 21.2106 21.518 21.7024 21.7639 21.8869 22.1328 22.1943 22.3172 22.3787 22.4402 22.6246 22.6861 22.7476 22.9935 22.9983 23.1165 23.2013 23.2394 23.4557 23.4854 23.6698 23.7016 23.7928 23.8209 23.8542 23.9157 24. 24.0387 24.1002 24.168 24.2231 24.3461 24.4076 24.4097 24.592 24.715 24.7722 24.8379 24.9609 25. 25.0027 25.0838 25.2683 25.3298 25.3912 25.3913 25.4527 25.5142 25.5757 25.6986 25.7601 25.8216 25.8831 26. 26.0675 26.129 26.1905 26.252 26.3134 26.3432 26.4364 26.5594 26.8053 26.9282 27. 27.0512 27.1127 27.1916 27.2717 27.4201 27.6045 27.666 27.9119 28.1578 28.2199 28.2766 28.4658 28.6496 28.6497 28.7726 28.8341 28.8961 29.0021 29.1415 29.203 29.2645 29.4489 29.5104 29.5719 29.6122 29.7563 29.7569 29.8178 29.8793 30. 30.0022 30.0028 30.0637 30.1252 30.2482 30.3096 30.3711 30.5556 30.6176 30.9244 30.9859 31.0474 31.1089 31.2318 31.2939 31.3548 31.4163 31.6601 31.8 31.9701 32.0311 32.2155 32.3385 32.3745 32.3894 32.4614 32.5844 32.8308 33.0768 33.5071 33.5395 33.5681 33.6916 33.8755 33.9375 34.3058 34.6133 34.6747 34.9206 35. 35.228 35.2896 35.351 35.4496 35.5354 35.5874 35.9043 35.9658 36.0273 36.1312 36.5191 36.5806 36.6421 36.765 37.011 37.2442 37.2569 37.3798 37.4413 37.5 37.5028 37.5643 38.0561 38.1176 38.1791 38.6094 39.0398 39.1352 39.2242 39.3472 39.4087 39.5316 39.5932 39.839 39.962 40. 40.085 40.2694 40.3309 40.4538 40.6022 40.6998 40.7612 40.8227 40.9075 40.9456 41.0072 41.0686 41.376 41.6834 41.8106 42.1732 42.2368 42.2982 42.4212 42.6374 42.6671 42.7392 43.036 43.159 43.8967 44. 44.0812 44.1426 44.2656 44.3886 44.45 44.4691 44.5115 44.6345 44.8762 45. 45.2493 45.4337 45.4952 45.6182 46.233 46.4174 46.4788 46.6018 46.7248 47.1552 47.4011 47.5855 47.6645 47.7085 48.0774 48.4462 48.8448 49.1501 49.3027 49.4299 49.6144 49.7373 49.7988 49.9218 50. 50.1677 50.2906 50.4136 50.5366 51.4588 51.5202 51.6432 52.0736 52.135 52.1965 52.258 52.381 52.8728 52.9343 53.8565 54.7177 55. 55.0866 55.394 55.5164 56.1927 56.2547 56.3157 56.5001 56.6846 56.8695 57.3608 57.4223 57.6688 57.7917 58.124 58.529 59.0208 59.5132 59.6356 59.7331 60. 60.066 60.4348 60.7428 60.8037 61.357 61.7264 62.2474 62.4637 62.7096 63.079 63.2014 63.8777 63.9397 64.431 65. 66.3984 66.5214 67.505 67.9969 68.1203 68.6732 68.9806 69.121 69.4724 70. 70.1487 70.4561 70.6914 70.702 70.9479 71.0094 71.0709 71.5627 71.8086 71.9316 72.6694 73.4686 73.5306 73.776 73.8767 74.5138 74.8 75. 75.1625 75.2515 76.1122 76.1319 76.2352 76.4202 76.4811 76.6041 77.3418 77.4648 78.0796 78.5714 78.5958 78.6668 78.8174 79.6781 80. 80.1699 80.9077 81.4462 81.7684 81.8914 82.1373 82.2475 83. 83.6213 85. 85.8261 86.3179 86.5638 87.0557 87.768 88.9616 89.6378 90. 90.2526 90.4487 91.6052 91.8172 92. 92.22 92.8348 93.8185 94.0644 94.1874 96.893 100. 100.9501 101.442 104.2245 104.516 105. 106.4834 106.9466 108.4507 108.5737 110. 110.2336 110.664 110.788 112.8778 115.152 117.7538 118.2875 120. 120.2549 121.3234 122.2222 124.1896 124.3126 124.7069 125. 125.911 126.8333 130.645 130.8294 131.6287 133.719 134.0269 135. 139.2522 139.4366 139.5596 141.1581 141.404 143.0025 143.3162 150. 153.3926 153.6396 153.9655 156.9584 157.4545 158.3004 159.4473 170. 173.8654 185.6696 189.7273 199.98 200. 202.5734 202.884 215.2415 241. 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V329: float32, 100, %89.98 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. nan] V330: float32, 56, %89.98 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. nan] V331: float32, 1542, %89.98 [0.0000e+00 5.0000e+00 6.0000e+00 ... 9.4901e+04 1.0406e+05 nan] V332: float32, 1867, %89.98 [0.00000e+00 5.00000e+00 7.00000e+00 ... 1.46317e+05 1.46358e+05 nan] V333: float32, 1625, %89.98 [0.00000e+00 5.00000e+00 7.00000e+00 ... 1.03495e+05 1.04060e+05 nan] V334: float32, 68, %89.98 [ 0. 5. 6. 7. 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3.780e+02 3.790e+02 3.800e+02 3.810e+02 3.840e+02 3.850e+02 3.890e+02 3.900e+02 3.940e+02 3.950e+02 3.990e+02 4.000e+02 4.010e+02 4.040e+02 4.050e+02 4.090e+02 4.100e+02 4.150e+02 4.160e+02 4.180e+02 4.200e+02 4.240e+02 4.250e+02 4.290e+02 4.300e+02 4.340e+02 4.350e+02 4.400e+02 4.430e+02 4.440e+02 4.450e+02 4.490e+02 4.500e+02 4.540e+02 4.550e+02 4.650e+02 4.700e+02 4.750e+02 4.800e+02 4.820e+02 4.850e+02 4.900e+02 5.000e+02 5.050e+02 5.100e+02 5.110e+02 5.120e+02 5.150e+02 5.250e+02 5.270e+02 5.300e+02 5.370e+02 5.400e+02 5.420e+02 5.450e+02 5.470e+02 5.480e+02 5.550e+02 5.590e+02 5.600e+02 5.650e+02 5.670e+02 5.690e+02 5.700e+02 5.750e+02 5.800e+02 5.850e+02 5.860e+02 5.900e+02 6.000e+02 6.050e+02 6.100e+02 6.300e+02 6.350e+02 6.400e+02 6.500e+02 6.650e+02 6.800e+02 6.850e+02 7.000e+02 7.050e+02 7.250e+02 7.300e+02 7.400e+02 7.450e+02 7.460e+02 7.500e+02 7.650e+02 7.700e+02 8.000e+02 8.110e+02 8.200e+02 8.350e+02 8.450e+02 8.660e+02 8.700e+02 8.850e+02 9.000e+02 9.150e+02 9.190e+02 9.250e+02 9.350e+02 9.500e+02 9.600e+02 9.720e+02 9.900e+02 9.950e+02 1.000e+03 1.019e+03 1.020e+03 1.040e+03 1.069e+03 1.075e+03 1.078e+03 1.090e+03 1.094e+03 1.095e+03 1.100e+03 1.105e+03 1.110e+03 1.115e+03 1.120e+03 1.125e+03 1.134e+03 1.140e+03 1.145e+03 1.153e+03 1.160e+03 1.184e+03 1.190e+03 1.200e+03 1.209e+03 1.210e+03 1.234e+03 1.259e+03 1.265e+03 1.269e+03 1.310e+03 1.335e+03 1.340e+03 1.369e+03 1.385e+03 1.410e+03 1.475e+03 1.500e+03 1.540e+03 1.600e+03 1.705e+03 1.760e+03 1.790e+03 1.886e+03 2.000e+03 2.036e+03 2.102e+03 2.117e+03 2.125e+03 2.147e+03 2.201e+03 2.246e+03 2.272e+03 2.301e+03 2.337e+03 2.356e+03 3.000e+03 6.375e+03 nan] V336: float32, 171, %89.98 [0.000e+00 5.000e+00 7.000e+00 8.000e+00 1.000e+01 1.200e+01 1.400e+01 1.500e+01 1.600e+01 1.700e+01 1.800e+01 2.000e+01 2.100e+01 2.200e+01 2.300e+01 2.400e+01 2.500e+01 2.600e+01 2.700e+01 2.800e+01 2.900e+01 3.000e+01 3.100e+01 3.300e+01 3.400e+01 3.500e+01 3.600e+01 3.700e+01 3.800e+01 4.000e+01 4.200e+01 4.300e+01 4.400e+01 4.500e+01 4.600e+01 5.000e+01 5.300e+01 5.400e+01 5.500e+01 5.600e+01 5.700e+01 5.900e+01 6.000e+01 6.500e+01 7.000e+01 7.200e+01 7.300e+01 7.500e+01 7.700e+01 8.000e+01 8.100e+01 8.300e+01 8.400e+01 8.500e+01 9.000e+01 9.100e+01 9.200e+01 9.500e+01 1.000e+02 1.050e+02 1.060e+02 1.070e+02 1.080e+02 1.100e+02 1.110e+02 1.150e+02 1.200e+02 1.210e+02 1.220e+02 1.250e+02 1.260e+02 1.270e+02 1.300e+02 1.350e+02 1.360e+02 1.400e+02 1.450e+02 1.500e+02 1.510e+02 1.520e+02 1.550e+02 1.560e+02 1.600e+02 1.620e+02 1.650e+02 1.660e+02 1.700e+02 1.710e+02 1.720e+02 1.750e+02 1.800e+02 1.820e+02 1.850e+02 1.860e+02 1.900e+02 1.950e+02 2.000e+02 2.020e+02 2.100e+02 2.150e+02 2.200e+02 2.250e+02 2.260e+02 2.400e+02 2.500e+02 2.550e+02 2.650e+02 2.750e+02 2.850e+02 2.900e+02 2.960e+02 3.000e+02 3.150e+02 3.160e+02 3.200e+02 3.250e+02 3.320e+02 3.400e+02 3.450e+02 3.500e+02 3.600e+02 3.800e+02 3.850e+02 3.900e+02 3.950e+02 4.000e+02 4.010e+02 4.100e+02 4.310e+02 4.380e+02 4.450e+02 4.500e+02 4.750e+02 4.850e+02 5.000e+02 5.130e+02 5.200e+02 5.350e+02 5.450e+02 5.500e+02 5.510e+02 5.550e+02 5.750e+02 5.810e+02 5.900e+02 6.000e+02 6.100e+02 6.190e+02 6.300e+02 6.310e+02 6.450e+02 6.500e+02 6.850e+02 7.000e+02 7.250e+02 7.290e+02 7.710e+02 7.750e+02 7.940e+02 8.180e+02 9.250e+02 9.420e+02 9.590e+02 9.680e+02 1.014e+03 1.044e+03 1.052e+03 1.058e+03 1.125e+03 2.125e+03 6.375e+03 nan] V337: float32, 172, %89.98 [0.0000e+00 5.0000e+00 6.0000e+00 7.0000e+00 8.0000e+00 1.0000e+01 1.1000e+01 1.2000e+01 1.5000e+01 1.6000e+01 1.7000e+01 1.8000e+01 2.0000e+01 2.5000e+01 2.6000e+01 3.0000e+01 3.1000e+01 3.4000e+01 3.5000e+01 3.9000e+01 4.0000e+01 4.2000e+01 4.4000e+01 4.5000e+01 5.0000e+01 5.1000e+01 5.2000e+01 5.3000e+01 5.5000e+01 6.0000e+01 6.8000e+01 6.9000e+01 7.0000e+01 7.5000e+01 8.0000e+01 8.3000e+01 9.0000e+01 9.5000e+01 9.8000e+01 1.0000e+02 1.0500e+02 1.0600e+02 1.1000e+02 1.1500e+02 1.2000e+02 1.2500e+02 1.3000e+02 1.4000e+02 1.4500e+02 1.5000e+02 1.6000e+02 1.6100e+02 1.6500e+02 1.7500e+02 1.8000e+02 2.0000e+02 2.1200e+02 2.2000e+02 2.2500e+02 2.3500e+02 2.5000e+02 2.6700e+02 2.7500e+02 3.0000e+02 3.0500e+02 3.1800e+02 3.2000e+02 3.3000e+02 3.5000e+02 3.7500e+02 3.7700e+02 4.0000e+02 4.2400e+02 4.2800e+02 4.3000e+02 4.3200e+02 4.3600e+02 4.4000e+02 4.5000e+02 4.6200e+02 4.7900e+02 4.8000e+02 5.0000e+02 5.2500e+02 5.3000e+02 5.3400e+02 5.4200e+02 5.5000e+02 5.5500e+02 5.8500e+02 6.0000e+02 6.2700e+02 6.3600e+02 6.4000e+02 6.4100e+02 6.4800e+02 6.6000e+02 6.8000e+02 6.9100e+02 7.0000e+02 7.4200e+02 7.4600e+02 7.5000e+02 7.5400e+02 7.5600e+02 7.9200e+02 8.0000e+02 8.0500e+02 8.1500e+02 8.2100e+02 8.2500e+02 8.4800e+02 8.5200e+02 8.6000e+02 8.6400e+02 8.8600e+02 9.0000e+02 9.5400e+02 9.5800e+02 9.6200e+02 9.7000e+02 1.0000e+03 1.0600e+03 1.0640e+03 1.0678e+03 1.0720e+03 1.0760e+03 1.1000e+03 1.1270e+03 1.1500e+03 1.1650e+03 1.1660e+03 1.1700e+03 1.1780e+03 1.1820e+03 1.2000e+03 1.2250e+03 1.2330e+03 1.2370e+03 1.2500e+03 1.2880e+03 1.3000e+03 1.3390e+03 1.3470e+03 1.3500e+03 1.3750e+03 1.3980e+03 1.4450e+03 1.4490e+03 1.4570e+03 1.5000e+03 1.5040e+03 1.5120e+03 1.5510e+03 1.6000e+03 1.8000e+03 2.0000e+03 2.2500e+03 2.4000e+03 2.5000e+03 3.0000e+03 4.2500e+03 4.4000e+03 4.5000e+03 5.0000e+03 6.7500e+03 7.5000e+03 9.0000e+03 1.0000e+04 2.1600e+04 5.2030e+04 1.0406e+05 nan] V338: float32, 247, %89.98 [0.0000e+00 5.0000e+00 8.0000e+00 1.0000e+01 1.1000e+01 1.5000e+01 1.7000e+01 1.8000e+01 2.0000e+01 2.4000e+01 2.5000e+01 2.6000e+01 2.7000e+01 3.0000e+01 3.4000e+01 3.5000e+01 4.0000e+01 4.1000e+01 4.2000e+01 4.5000e+01 5.0000e+01 5.5000e+01 6.0000e+01 6.7000e+01 6.9000e+01 7.0000e+01 7.5000e+01 7.7000e+01 8.0000e+01 8.6000e+01 8.7000e+01 8.8000e+01 9.0000e+01 1.0000e+02 1.0300e+02 1.0500e+02 1.0700e+02 1.1000e+02 1.1200e+02 1.1300e+02 1.1500e+02 1.1800e+02 1.2000e+02 1.2500e+02 1.3000e+02 1.3300e+02 1.3700e+02 1.4000e+02 1.4100e+02 1.4200e+02 1.4800e+02 1.5000e+02 1.5200e+02 1.5500e+02 1.5600e+02 1.6000e+02 1.6200e+02 1.6300e+02 1.6700e+02 1.7000e+02 1.7200e+02 1.7500e+02 1.8000e+02 1.8200e+02 1.8700e+02 1.9000e+02 1.9200e+02 1.9700e+02 2.0000e+02 2.0700e+02 2.1000e+02 2.2000e+02 2.2500e+02 2.2700e+02 2.3000e+02 2.3500e+02 2.4000e+02 2.4700e+02 2.5000e+02 2.7000e+02 2.7500e+02 2.7700e+02 2.8000e+02 2.9500e+02 2.9700e+02 3.0000e+02 3.0700e+02 3.1700e+02 3.2000e+02 3.2200e+02 3.2700e+02 3.3200e+02 3.3700e+02 3.4100e+02 3.4600e+02 3.5000e+02 3.6600e+02 3.7400e+02 3.7500e+02 3.8600e+02 4.0000e+02 4.4100e+02 4.5000e+02 4.8000e+02 5.0000e+02 5.5000e+02 5.6600e+02 6.0000e+02 6.4000e+02 6.4100e+02 6.9100e+02 7.5000e+02 8.0000e+02 8.1600e+02 8.2500e+02 9.0000e+02 9.4100e+02 1.0000e+03 1.0660e+03 1.0678e+03 1.1000e+03 1.1500e+03 1.1910e+03 1.2000e+03 1.2500e+03 1.2560e+03 1.3000e+03 1.3500e+03 1.3620e+03 1.3750e+03 1.4000e+03 1.4170e+03 1.5000e+03 1.5230e+03 1.6000e+03 1.6330e+03 1.6880e+03 1.7500e+03 1.7980e+03 1.9040e+03 2.0000e+03 2.0100e+03 2.1160e+03 2.2220e+03 2.2500e+03 2.3280e+03 2.3500e+03 2.4000e+03 2.4340e+03 2.5000e+03 2.5400e+03 2.6460e+03 2.7520e+03 2.8580e+03 2.9500e+03 2.9680e+03 3.0000e+03 3.0780e+03 3.1880e+03 3.2940e+03 3.3490e+03 3.4550e+03 3.5500e+03 3.5610e+03 3.6670e+03 3.7770e+03 3.8870e+03 3.9970e+03 4.0000e+03 4.0520e+03 4.1500e+03 4.1580e+03 4.2500e+03 4.2640e+03 4.3700e+03 4.4000e+03 4.4760e+03 4.5000e+03 4.5820e+03 4.6920e+03 4.7500e+03 4.7980e+03 4.9040e+03 5.0000e+03 5.0100e+03 5.1160e+03 5.2000e+03 5.2220e+03 5.2770e+03 5.3830e+03 5.4890e+03 5.5540e+03 5.6190e+03 5.6840e+03 5.7000e+03 5.8490e+03 6.0140e+03 6.1200e+03 6.2260e+03 6.3320e+03 6.4380e+03 6.5440e+03 6.6500e+03 6.7500e+03 6.7560e+03 6.8620e+03 6.9680e+03 7.0740e+03 7.1800e+03 7.2860e+03 7.3920e+03 7.4980e+03 7.5000e+03 7.6040e+03 7.7090e+03 7.8150e+03 7.9210e+03 8.0000e+03 8.0270e+03 8.1330e+03 8.2390e+03 8.3450e+03 8.4510e+03 8.5000e+03 8.5570e+03 8.6630e+03 8.7690e+03 8.8750e+03 8.9850e+03 9.0000e+03 9.0950e+03 9.2050e+03 9.3150e+03 9.4250e+03 9.5000e+03 9.5350e+03 9.6410e+03 9.7470e+03 9.8530e+03 9.9500e+03 9.9590e+03 1.0000e+04 1.0065e+04 1.1250e+04 2.1600e+04 5.2030e+04 1.0406e+05 nan] V339: float32, 236, %89.98 [0.0000e+00 5.0000e+00 7.0000e+00 8.0000e+00 1.0000e+01 1.1000e+01 1.5000e+01 1.7000e+01 1.8000e+01 2.0000e+01 2.4000e+01 2.5000e+01 2.6000e+01 2.9000e+01 3.0000e+01 3.4000e+01 3.5000e+01 3.7000e+01 4.0000e+01 4.2000e+01 4.5000e+01 4.7000e+01 5.0000e+01 5.1000e+01 5.2000e+01 5.5000e+01 6.0000e+01 6.5000e+01 6.7000e+01 6.9000e+01 7.0000e+01 7.5000e+01 7.6000e+01 8.0000e+01 8.2000e+01 8.3000e+01 8.5000e+01 8.6000e+01 8.7000e+01 8.8000e+01 9.0000e+01 9.5000e+01 9.7000e+01 9.8000e+01 1.0000e+02 1.0300e+02 1.0500e+02 1.0600e+02 1.0700e+02 1.1000e+02 1.1300e+02 1.1500e+02 1.2000e+02 1.2500e+02 1.2700e+02 1.3000e+02 1.3500e+02 1.4000e+02 1.5000e+02 1.6000e+02 1.6100e+02 1.6500e+02 1.7000e+02 1.7500e+02 1.8000e+02 1.8500e+02 1.9000e+02 2.0000e+02 2.0500e+02 2.1000e+02 2.1500e+02 2.2000e+02 2.2500e+02 2.3500e+02 2.4500e+02 2.5000e+02 2.6000e+02 2.6900e+02 2.7000e+02 2.7100e+02 2.7400e+02 2.7500e+02 2.7700e+02 2.8000e+02 3.0000e+02 3.0500e+02 3.2000e+02 3.5000e+02 3.7500e+02 4.0000e+02 4.3000e+02 4.5000e+02 4.8000e+02 5.0000e+02 5.3000e+02 5.5000e+02 5.5500e+02 6.0000e+02 6.4000e+02 6.4100e+02 6.8000e+02 7.0000e+02 7.5000e+02 8.0000e+02 8.0500e+02 8.2500e+02 8.7000e+02 9.0000e+02 9.7600e+02 1.0000e+03 1.0310e+03 1.0678e+03 1.1000e+03 1.1370e+03 1.1500e+03 1.1900e+03 1.2000e+03 1.2470e+03 1.2500e+03 1.3000e+03 1.3020e+03 1.3500e+03 1.3750e+03 1.4120e+03 1.5000e+03 1.5180e+03 1.6000e+03 1.6240e+03 1.7300e+03 1.8000e+03 1.8360e+03 1.9420e+03 2.0000e+03 2.0480e+03 2.1540e+03 2.2500e+03 2.2600e+03 2.3560e+03 2.3660e+03 2.4000e+03 2.4720e+03 2.5000e+03 2.5820e+03 2.6920e+03 2.8020e+03 2.9080e+03 2.9110e+03 2.9630e+03 2.9910e+03 3.0000e+03 3.0170e+03 3.0690e+03 3.0970e+03 3.1230e+03 3.1750e+03 3.1910e+03 3.2030e+03 3.2290e+03 3.2500e+03 3.2810e+03 3.3010e+03 3.3090e+03 3.3350e+03 3.3560e+03 3.3910e+03 3.4110e+03 3.4150e+03 3.4410e+03 3.4620e+03 3.5010e+03 3.5210e+03 3.5470e+03 3.5680e+03 3.6000e+03 3.6110e+03 3.6310e+03 3.6530e+03 3.6570e+03 3.6660e+03 3.6740e+03 3.7120e+03 3.7410e+03 3.7590e+03 3.7720e+03 3.7800e+03 3.8650e+03 3.8780e+03 3.8860e+03 3.9710e+03 3.9840e+03 3.9920e+03 4.0420e+03 4.0770e+03 4.0900e+03 4.0980e+03 4.1960e+03 4.2040e+03 4.2500e+03 4.2540e+03 4.3060e+03 4.3100e+03 4.3600e+03 4.4000e+03 4.4120e+03 4.4150e+03 4.4280e+03 4.5000e+03 4.5180e+03 4.6240e+03 4.7300e+03 4.7580e+03 4.8360e+03 4.8640e+03 4.8910e+03 4.9700e+03 4.9970e+03 5.0000e+03 5.0760e+03 5.1030e+03 5.1680e+03 5.1820e+03 5.2330e+03 5.2880e+03 5.2980e+03 5.4630e+03 5.5730e+03 5.6280e+03 6.7500e+03 7.5000e+03 8.5000e+03 9.0000e+03 1.0000e+04 1.1250e+04 2.1600e+04 5.2030e+04 1.0406e+05 nan]
# removing high correlated variables (222 eliminated)
corr_treshold = 0.75
drop_col = remove_collinear_features(train[columns],corr_treshold)
len(drop_col)
222
# dropping redundant Vs
train = train.drop(drop_col, axis=1)
test = test.drop(drop_col, axis=1)
# remaining Vs lenght (64 vars)
columns=[col for col in train.columns if re.search('^V\d*', col)]
len(columns)
64
plt.figure(figsize=(10,10))
sns.heatmap(train[columns+['isFraud']].sample(frac=0.2).corr(),annot=False, cmap="RdBu_r")
<Axes: >
#pickling datasets
#Save 'train' data to a pickle file named 'train_2.pkl'
train.to_pickle(r'C:\Fraud_Data\data\train_2.pkl')
#save 'test' data to a pickle file named 'test_2.pkl'
test.to_pickle(r'C:\Fraud_Data\data\test_2.pkl')
# Read the 'train_2.pkl' pickle file and load it into the 'train' DataFrame
train = pd.read_pickle('./train_2.pkl')
# Read the 'test_2.pkl' pickle file and load it into the 'test' DataFrame
test = pd.read_pickle('./test_2.pkl')
column_details(regex='id_(1|2|3|4|5|6|7|8|9|10|11)$', df=train)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE id_10: float64, 59, %86.74 [-100. -68. -64. -60. -59. -58. -57. -56. -55. -54. -51. -50. -49. -47. -44. -43. -42. -41. -40. -39. -38. -37. -36. -35. -34. -33. -32. -31. -30. -29. -28. -27. -26. -25. -24. -23. -22. -21. -20. -19. -18. -17. -16. -15. -14. -13. -12. -11. -10. -9. -8. -7. -6. -5. -4. -3. -2. -1. 0. nan] id_11: float64, 346, %74.59 [ 90. 90.09999847 90.11000061 90.16000366 90.19999695 90.20999908 90.23999786 90.26999664 90.27999878 90.29000092 90.31999969 90.34999847 90.36000061 90.37999725 90.41000366 90.43000031 90.48000336 90.51999664 90.52999878 90.54000092 90.55000305 90.56999969 90.58999634 90.62999725 90.65000153 90.69999695 90.73999786 90.76999664 90.80000305 90.83000183 90.84999847 90.91000366 91. 91.02999878 91.06999969 91.08999634 91.11000061 91.13999939 91.15000153 91.18000031 91.20999908 91.25 91.27999878 91.30000305 91.33999634 91.36000061 91.40000153 91.43000031 91.44999695 91.45999908 91.48999786 91.51000214 91.52999878 91.56999969 91.58000183 91.58999634 91.59999847 91.66999817 91.73999786 91.76000214 91.77999878 91.80000305 91.81999969 91.87999725 91.88999939 91.91999817 91.94000244 91.94999695 92. 92.05999756 92.08000183 92.11000061 92.12999725 92.19000244 92.20999908 92.23000336 92.23999786 92.30999756 92.37000275 92.41000366 92.44999695 92.5 92.54000092 92.55000305 92.55999756 92.58999634 92.62000275 92.62999725 92.68000031 92.69999695 92.70999908 92.73000336 92.73999786 92.75 92.76999664 92.77999878 92.86000061 92.88999939 92.94000244 93. 93.01999664 93.05999756 93.09999847 93.13999939 93.15000153 93.18000031 93.22000122 93.23999786 93.26000214 93.33000183 93.37000275 93.38999939 93.40000153 93.41999817 93.44000244 93.48000336 93.55000305 93.58000183 93.58999634 93.62000275 93.65000153 93.66999817 93.68000031 93.75 93.80999756 93.83000183 93.84999847 93.88999939 93.94000244 93.95999908 93.97000122 93.98000336 94. 94.02999878 94.05000305 94.05999756 94.12000275 94.16000366 94.16999817 94.19000244 94.19999695 94.23000336 94.25 94.29000092 94.31999969 94.37000275 94.37999725 94.40000153 94.44000244 94.5 94.51999664 94.52999878 94.55000305 94.56999969 94.58999634 94.62000275 94.63999939 94.66999817 94.68000031 94.73999786 94.76000214 94.79000092 94.80999756 94.83000183 94.87000275 94.91999817 94.93000031 94.94000244 94.98000336 95. 95.04000092 95.05000305 95.05999756 95.06999969 95.08000183 95.11000061 95.15000153 95.16000366 95.18000031 95.19000244 95.20999908 95.23999786 95.26000214 95.27999878 95.29000092 95.30999756 95.34999847 95.36000061 95.37999725 95.40000153 95.41000366 95.41999817 95.44999695 95.51000214 95.55999756 95.58999634 95.59999847 95.65000153 95.69000244 95.69999695 95.70999908 95.73999786 95.76999664 95.80000305 95.80999756 95.83000183 95.86000061 95.88999939 95.94000244 95.95999908 96. 96.02999878 96.04000092 96.05000305 96.08000183 96.09999847 96.12000275 96.15000153 96.19000244 96.19999695 96.23000336 96.25 96.26000214 96.30000305 96.33999634 96.36000061 96.38999939 96.40000153 96.43000031 96.47000122 96.48999786 96.51000214 96.55000305 96.58999634 96.62999725 96.63999939 96.66999817 96.69000244 96.69999695 96.72000122 96.73999786 96.76999664 96.83000183 96.83999634 96.87999725 96.91999817 96.94000244 96.97000122 97. 97.01000214 97.02999878 97.05999756 97.08999634 97.09999847 97.11000061 97.12000275 97.13999939 97.16000366 97.16999817 97.19999695 97.22000122 97.23999786 97.25 97.26000214 97.26999664 97.30000305 97.33000183 97.34999847 97.37000275 97.38999939 97.40000153 97.41000366 97.44000244 97.44999695 97.47000122 97.5 97.51999664 97.52999878 97.54000092 97.55000305 97.55999756 97.58999634 97.62000275 97.65000153 97.66000366 97.66999817 97.69999695 97.73000336 97.75 97.77999878 97.80000305 97.83000183 97.84999847 97.87000275 97.88999939 97.94000244 97.95999908 97.97000122 97.98000336 97.98999786 98. 98.01999664 98.02999878 98.05999756 98.08000183 98.09999847 98.11000061 98.12999725 98.15000153 98.18000031 98.19999695 98.25 98.29000092 98.30999756 98.31999969 98.33000183 98.33999634 98.34999847 98.36000061 98.38999939 98.40000153 98.41000366 98.44000244 98.48000336 98.51000214 98.51999664 98.54000092 98.58999634 98.69000244 98.69999695 98.73000336 98.76999664 98.81999969 98.90000153 98.91000366 98.91999817 98.93000031 98.94000244 98.94999695 98.98000336 99.01000214 99.04000092 99.09999847 99.13999939 99.22000122 100. nan]
# removing high correlated variables
corr_treshold = 0.75
drop_col = remove_collinear_features(train[columns],corr_treshold)
len(drop_col)
0
plt.figure(figsize=(10,10))
sns.heatmap(train[columns].sample(frac=0.2).corr(),annot=False, cmap="RdBu_r")
<Axes: >
There is no correlation between these two variables, we are remaining them.
column_details(regex='id_(12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|32|33|34|35|36|37|38)', df=train)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE id_12: object, 2, %74.02 ['Found' 'NotFound' nan] id_13: object, 52, %77.38 ['10.0' '11.0' '12.0' '13.0' '14.0' '15.0' '17.0' '18.0' '19.0' '20.0' '21.0' '22.0' '23.0' '24.0' '25.0' '26.0' '28.0' '29.0' '30.0' '31.0' '32.0' '33.0' '34.0' '35.0' '36.0' '37.0' '38.0' '39.0' '40.0' '41.0' '43.0' '44.0' '45.0' '46.0' '47.0' '48.0' '49.0' '50.0' '51.0' '52.0' '53.0' '54.0' '55.0' '56.0' '57.0' '58.0' '59.0' '60.0' '61.0' '62.0' '63.0' '64.0' nan] id_14: object, 25, %85.14 ['-120.0' '-180.0' '-210.0' '-240.0' '-300.0' '-360.0' '-420.0' '-480.0' '-540.0' '-600.0' '-660.0' '0.0' '120.0' '180.0' '240.0' '270.0' '300.0' '330.0' '360.0' '420.0' '480.0' '540.0' '60.0' '600.0' '720.0' nan] id_15: object, 3, %74.59 ['Found' 'New' 'Unknown' nan] id_16: object, 2, %76.65 ['Found' 'NotFound' nan] id_17: object, 101, %74.83 ['100.0' '101.0' '102.0' '105.0' '106.0' '107.0' '110.0' '111.0' '112.0' '114.0' '116.0' '117.0' '118.0' '119.0' 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'282.0' '283.0' '284.0' '287.0' '288.0' '289.0' '290.0' '291.0' '292.0' '294.0' '295.0' '296.0' '297.0' '298.0' '299.0' '300.0' '301.0' '302.0' '303.0' '304.0' '305.0' '306.0' '307.0' '308.0' '309.0' '310.0' '311.0' '312.0' '313.0' '314.0' '315.0' '316.0' '317.0' '318.0' '319.0' '320.0' '321.0' '322.0' '324.0' '325.0' '326.0' '327.0' '328.0' '330.0' '332.0' '333.0' '335.0' '337.0' '339.0' '340.0' '341.0' '342.0' '343.0' '344.0' '345.0' '346.0' '347.0' '348.0' '350.0' '351.0' '352.0' '353.0' '355.0' '356.0' '358.0' '359.0' '360.0' '361.0' '362.0' '363.0' '364.0' '365.0' '366.0' '367.0' '368.0' '369.0' '370.0' '371.0' '372.0' '373.0' '374.0' '375.0' '376.0' '377.0' '378.0' '379.0' '380.0' '381.0' '382.0' '383.0' '384.0' '385.0' '387.0' '388.0' '389.0' '390.0' '391.0' '393.0' '394.0' '395.0' '396.0' '397.0' '398.0' '399.0' '400.0' '401.0' '402.0' '403.0' '404.0' '405.0' '406.0' '407.0' '409.0' '410.0' '411.0' '412.0' '413.0' '414.0' '415.0' '416.0' '417.0' '418.0' '419.0' '420.0' '421.0' '422.0' '424.0' '425.0' '426.0' '427.0' '428.0' '429.0' '430.0' '431.0' '432.0' '433.0' '434.0' '435.0' '436.0' '437.0' '438.0' '439.0' '440.0' '441.0' '442.0' '443.0' '444.0' '445.0' '446.0' '447.0' '449.0' '451.0' '452.0' '453.0' '454.0' '455.0' '456.0' '457.0' '458.0' '459.0' '460.0' '462.0' '463.0' '466.0' '467.0' '468.0' '469.0' '471.0' '472.0' '473.0' '474.0' '475.0' '476.0' '478.0' '479.0' '480.0' '481.0' '482.0' '483.0' '484.0' '485.0' '487.0' '488.0' '489.0' '490.0' '491.0' '492.0' '493.0' '494.0' '495.0' '496.0' '498.0' '499.0' '500.0' '502.0' '503.0' '505.0' '506.0' '507.0' '508.0' '509.0' '510.0' '511.0' '512.0' '514.0' '515.0' '516.0' '517.0' '519.0' '520.0' '521.0' '522.0' '523.0' '524.0' '525.0' '526.0' '527.0' '528.0' '529.0' '530.0' '531.0' '532.0' '533.0' '535.0' '536.0' '537.0' '538.0' '539.0' '541.0' '542.0' '543.0' '544.0' '545.0' '546.0' '547.0' '548.0' '549.0' '550.0' '551.0' '552.0' '553.0' '554.0' '555.0' '556.0' '558.0' '559.0' '560.0' '561.0' '562.0' '563.0' 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'133.0' '134.0' '136.0' '138.0' '139.0' '142.0' '143.0' '144.0' '145.0' '146.0' '150.0' '152.0' '153.0' '154.0' '155.0' '160.0' '161.0' '163.0' '165.0' '166.0' '167.0' '168.0' '170.0' '173.0' '176.0' '177.0' '178.0' '180.0' '181.0' '182.0' '184.0' '185.0' '186.0' '187.0' '188.0' '190.0' '191.0' '192.0' '193.0' '194.0' '196.0' '197.0' '201.0' '202.0' '203.0' '204.0' '208.0' '209.0' '211.0' '213.0' '214.0' '215.0' '216.0' '221.0' '222.0' '223.0' '225.0' '228.0' '232.0' '235.0' '236.0' '239.0' '240.0' '241.0' '242.0' '243.0' '244.0' '245.0' '246.0' '247.0' '248.0' '249.0' '251.0' '252.0' '253.0' '254.0' '256.0' '258.0' '259.0' '261.0' '262.0' '265.0' '266.0' '268.0' '269.0' '270.0' '271.0' '272.0' '273.0' '274.0' '277.0' '278.0' '279.0' '280.0' '282.0' '283.0' '285.0' '286.0' '288.0' '289.0' '290.0' '292.0' '296.0' '298.0' '299.0' '300.0' '301.0' '303.0' '304.0' '305.0' '306.0' '307.0' '308.0' '310.0' '311.0' '312.0' '313.0' '314.0' '315.0' '316.0' '318.0' '320.0' '321.0' '322.0' '323.0' '324.0' '325.0' '327.0' '328.0' '329.0' '330.0' '332.0' '333.0' '335.0' '336.0' '337.0' '339.0' '340.0' '341.0' '342.0' '343.0' '345.0' '346.0' '347.0' '348.0' '349.0' '350.0' '351.0' '352.0' '354.0' '355.0' '356.0' '357.0' '358.0' '359.0' '360.0' '366.0' '367.0' '368.0' '369.0' '370.0' '371.0' '372.0' '373.0' '374.0' '376.0' '377.0' '379.0' '380.0' '381.0' '383.0' '384.0' '385.0' '387.0' '390.0' '391.0' '392.0' '393.0' '394.0' '396.0' '397.0' '400.0' '401.0' '404.0' '407.0' '408.0' '410.0' '411.0' '414.0' '415.0' '417.0' '420.0' '423.0' '424.0' '426.0' '427.0' '428.0' '429.0' '430.0' '431.0' '432.0' '433.0' '435.0' '439.0' '440.0' '442.0' '443.0' '444.0' '445.0' '446.0' '447.0' '448.0' '449.0' '451.0' '452.0' '455.0' '456.0' '458.0' '459.0' '460.0' '462.0' '463.0' '464.0' '466.0' '468.0' '469.0' '471.0' '472.0' '473.0' '474.0' '476.0' '477.0' '478.0' '480.0' '481.0' '483.0' '484.0' '485.0' '486.0' '487.0' '489.0' '490.0' '491.0' '494.0' '496.0' '497.0' '498.0' '499.0' '500.0' '501.0' '505.0' '506.0' '507.0' '508.0' '509.0' '511.0' '514.0' '517.0' '521.0' '525.0' '526.0' '527.0' '530.0' '531.0' '532.0' '533.0' '535.0' '536.0' '537.0' '538.0' '539.0' '540.0' '541.0' '543.0' '545.0' '547.0' '549.0' '550.0' '552.0' '553.0' '554.0' '556.0' '559.0' '560.0' '561.0' '562.0' '563.0' '564.0' '565.0' '566.0' '567.0' '568.0' '569.0' '570.0' '572.0' '575.0' '576.0' '581.0' '583.0' '587.0' '588.0' '590.0' '593.0' '595.0' '596.0' '597.0' '598.0' '600.0' '602.0' '604.0' '606.0' '607.0' '609.0' '610.0' '611.0' '612.0' '615.0' '619.0' '620.0' '623.0' '626.0' '628.0' '629.0' '630.0' '632.0' '633.0' '634.0' '635.0' '636.0' '638.0' '639.0' '640.0' '641.0' '642.0' '643.0' '644.0' '645.0' '648.0' '649.0' '650.0' '655.0' '656.0' '657.0' '659.0' '660.0' '661.0' nan] id_28: object, 2, %74.59 ['Found' 'New' nan] id_29: object, 2, %74.59 ['Found' 'NotFound' nan] id_30: object, 72, %85.6 ['Android' 'Android 4.4.2' 'Android 5.0' 'Android 5.0.2' 'Android 5.1.1' 'Android 6.0' 'Android 6.0.1' 'Android 7.0' 'Android 7.1.1' 'Android 7.1.2' 'Android 8.0.0' 'Android 8.1.0' 'Linux' 'Mac' 'Mac OS X 10.10' 'Mac OS X 10.11' 'Mac OS X 10.12' 'Mac OS X 10.13' 'Mac OS X 10.6' 'Mac OS X 10.9' 'Mac OS X 10_10_5' 'Mac OS X 10_11_3' 'Mac OS X 10_11_4' 'Mac OS X 10_11_5' 'Mac OS X 10_11_6' 'Mac OS X 10_12' 'Mac OS X 10_12_1' 'Mac OS X 10_12_2' 'Mac OS X 10_12_3' 'Mac OS X 10_12_4' 'Mac OS X 10_12_5' 'Mac OS X 10_12_6' 'Mac OS X 10_13_1' 'Mac OS X 10_13_2' 'Mac OS X 10_13_3' 'Mac OS X 10_13_4' 'Mac OS X 10_13_5' 'Mac OS X 10_6_8' 'Mac OS X 10_7_5' 'Mac OS X 10_8_5' 'Mac OS X 10_9_5' 'Windows' 'Windows 10' 'Windows 7' 'Windows 8' 'Windows 8.1' 'Windows Vista' 'Windows XP' 'func' 'iOS' 'iOS 10.0.2' 'iOS 10.1.1' 'iOS 10.2.0' 'iOS 10.2.1' 'iOS 10.3.1' 'iOS 10.3.2' 'iOS 10.3.3' 'iOS 11.0.0' 'iOS 11.0.1' 'iOS 11.0.2' 'iOS 11.0.3' 'iOS 11.1.0' 'iOS 11.1.1' 'iOS 11.1.2' 'iOS 11.2.0' 'iOS 11.2.1' 'iOS 11.2.2' 'iOS 11.2.5' 'iOS 11.2.6' 'iOS 11.3.0' 'iOS 9.3.5' 'other' nan] id_31: object, 110, %74.66 ['BLU/Dash' 'Cherry' 'Generic/Android' 'Generic/Android 7.0' 'Inco/Minion' 'LG/K-200' 'Lanix/Ilium' 'M4Tel/M4' 'Microsoft/Windows' 'Mozilla/Firefox' 'Nokia/Lumia' 'Samsung/SCH' 'Samsung/SM-G531H' 'Samsung/SM-G532M' 'ZTE/Blade' 'android browser 4.0' 'android webview 4.0' 'aol' 'chrome' 'chrome 43.0 for android' 'chrome 46.0 for android' 'chrome 49.0' 'chrome 49.0 for android' 'chrome 50.0 for android' 'chrome 51.0' 'chrome 51.0 for android' 'chrome 52.0 for android' 'chrome 53.0 for android' 'chrome 54.0 for android' 'chrome 55.0' 'chrome 55.0 for android' 'chrome 56.0' 'chrome 56.0 for android' 'chrome 57.0' 'chrome 57.0 for android' 'chrome 58.0' 'chrome 58.0 for android' 'chrome 59.0' 'chrome 59.0 for android' 'chrome 60.0' 'chrome 60.0 for android' 'chrome 61.0' 'chrome 61.0 for android' 'chrome 62.0' 'chrome 62.0 for android' 'chrome 62.0 for ios' 'chrome 63.0' 'chrome 63.0 for android' 'chrome 63.0 for ios' 'chrome 64.0' 'chrome 64.0 for android' 'chrome 64.0 for ios' 'chrome 65.0' 'chrome 65.0 for android' 'chrome 65.0 for ios' 'chrome generic' 'chrome generic for android' 'chromium' 'comodo' 'cyberfox' 'edge' 'edge 13.0' 'edge 14.0' 'edge 15.0' 'edge 16.0' 'firefox' 'firefox 47.0' 'firefox 48.0' 'firefox 52.0' 'firefox 55.0' 'firefox 56.0' 'firefox 57.0' 'firefox 58.0' 'firefox 59.0' 'firefox generic' 'icedragon' 'ie' 'ie 11.0 for desktop' 'ie 11.0 for tablet' 'iron' 'line' 'maxthon' 'mobile' 'mobile safari 10.0' 'mobile safari 11.0' 'mobile safari 8.0' 'mobile safari 9.0' 'mobile safari generic' 'mobile safari uiwebview' 'opera' 'opera 49.0' 'opera 51.0' 'opera generic' 'other' 'palemoon' 'puffin' 'safari' 'safari generic' 'samsung' 'samsung browser 4.0' 'samsung browser 4.2' 'samsung browser 5.2' 'samsung browser 5.4' 'samsung browser 6.2' 'samsung browser 6.4' 'samsung browser 7.0' 'samsung browser generic' 'seamonkey' 'silk' 'waterfox' nan] id_32: object, 4, %85.6 ['0.0' '16.0' '24.0' '32.0' nan] id_33: object, 198, %86.57 ['0x0' '1024x600' '1024x640' '1024x768' '1062x630' '1093x615' '1120x700' '1136x640' '1138x640' '1152x648' '1152x720' '1152x864' '1184x720' '1188x720' '1200x675' '1200x720' '1229x691' '1232x800' '1239x697' '1264x924' '1279x1023' '1279x1024' '1280x1023' '1280x1024' '1280x1025' '1280x600' '1280x620' '1280x712' '1280x720' '1280x768' '1280x800' '1280x960' '1281x720' '1281x721' '1281x800' '1281x801' '1296x774' '1334x750' '1344x756' '1344x840' '1356x900' '1360x768' '1364x768' '1365x768' '1366x1024' '1366x767' '1366x768' '1368x768' '1371x857' '1400x1050' '1400x900' '1408x792' '1408x880' '1422x889' '1439x809' '1440x720' '1440x759' '1440x800' '1440x803' '1440x810' '1440x900' '1440x960' '1441x901' '1480x720' '1502x844' '1512x945' '1536x1152' '1536x864' '1536x960' '1584x990' '1599x900' '1600x1000' '1600x1024' '1600x1200' '1600x837' '1600x899' '1600x900' '1624x1080' '1638x922' '1658x946' '1679x1049' '1680x1049' '1680x1050' '1680x1051' '1680x945' '1684x947' '1700x960' '1720x1440' '1728x972' '1760x990' '1768x992' '1776x1000' '1776x1080' '1800x1125' '1805x1015' '1824x1026' '1848x1155' '1912x1025' '1919x1079' '1919x1080' '1919x1199' '1919x1200' '1920x1018' '1920x1079' '1920x1080' '1920x1081' '1920x1200' '1920x1201' '1920x1280' '1920x1281' '1920x975' '1921x1081' '2000x1125' '2001x1125' '2010x1080' '2048x1080' '2048x1152' '2048x1280' '2048x1536' '2049x1152' '2076x1080' '2112x1188' '2160x1215' '2160x1350' '2160x1440' '2208x1242' '2220x1080' '2220x1081' '2224x1668' '2304x1296' '2304x1440' '2368x1440' '2392x1440' '2400x1350' '2400x1600' '2436x1125' '2520x1575' '2552x1337' '2560x1080' '2560x1440' '2560x1600' '2560x1700' '2560x1800' '2562x1442' '2672x1440' '2700x1800' '2732x2048' '2735x1825' '2736x1824' '2800x1575' '2816x1584' '2816x1760' '2880x1440' '2880x1442' '2880x1620' '2880x1800' '2960x1440' '2961x1440' '2961x1442' '3000x2000' '3072x1728' '3200x1800' '3200x2000' '3201x1800' '3240x2160' '3360x1050' '3360x1890' '3360x2100' '3440x1440' '3441x1440' '3520x1980' '3600x2250' '3840x1080' '3840x1600' '3840x2160' '3840x2400' '3841x2161' '3843x2163' '4096x2304' '4500x3000' '5040x3150' '5120x2880' '5760x1080' '5760x3240' '600x450' '6016x3384' '6400x3600' '640x360' '6720x3780' '7500x5000' '768x576' '800x600' '801x480' '855x480' '921x691' '960x540' '960x640' '976x600' nan] id_34: object, 4, %85.59 ['match_status:-1' 'match_status:0' 'match_status:1' 'match_status:2' nan] id_35: object, 2, %74.59 ['F' 'T' nan] id_36: object, 2, %74.59 ['F' 'T' nan] id_37: object, 2, %74.59 ['F' 'T' nan] id_38: object, 2, %74.59 ['F' 'T' nan]
# Syncronize train and test
for col in [ 'id_12', 'id_13', 'id_14','id_15', 'id_17', 'id_19', 'id_30', 'id_31', 'id_32', 'id_34', 'id_36', 'id_37', 'id_38']:
old_versions_col= set(train[col].unique()) - set(test[col].unique())
new_versions_col = set(test[col].unique()) - set(train[col].unique())
test[col] =test[col].apply(lambda x: np.nan if x in new_versions_col else x)
train[col] =train[col].apply(lambda x: np.nan if x in old_versions_col else x)
rareIds = []
# Specify the range of columns you want to process
columns_to_process = ['id_12', 'id_13', 'id_14',
'id_15', 'id_17', 'id_19', 'id_30', 'id_31', 'id_32', 'id_34', 'id_36', 'id_37', 'id_38']
for col in columns_to_process:
for k, df in enumerate([train, test]):
rareIds = df[col].value_counts()
rareIds = [value for value in rareIds.index if rareIds[value] < 3]
rareIds += rareIds
print(f"{('TEST' if k else 'TRAIN')}")
print(f"Number of unique in {col}: {df[col].nunique()}")
print(f"Number of unique values with frequency less than 3 in {col}: {len(rareIds)}\n")
rareIds = set(rareIds)
TRAIN Number of unique in id_12: 2 Number of unique values with frequency less than 3 in id_12: 0 TEST Number of unique in id_12: 2 Number of unique values with frequency less than 3 in id_12: 0 TRAIN Number of unique in id_13: 19 Number of unique values with frequency less than 3 in id_13: 2 TEST Number of unique in id_13: 19 Number of unique values with frequency less than 3 in id_13: 2 TRAIN Number of unique in id_14: 18 Number of unique values with frequency less than 3 in id_14: 0 TEST Number of unique in id_14: 18 Number of unique values with frequency less than 3 in id_14: 4 TRAIN Number of unique in id_15: 3 Number of unique values with frequency less than 3 in id_15: 0 TEST Number of unique in id_15: 3 Number of unique values with frequency less than 3 in id_15: 0 TRAIN Number of unique in id_17: 59 Number of unique values with frequency less than 3 in id_17: 12 TEST Number of unique in id_17: 59 Number of unique values with frequency less than 3 in id_17: 34 TRAIN Number of unique in id_19: 366 Number of unique values with frequency less than 3 in id_19: 36 TEST Number of unique in id_19: 366 Number of unique values with frequency less than 3 in id_19: 242 TRAIN Number of unique in id_30: 68 Number of unique values with frequency less than 3 in id_30: 2 TEST Number of unique in id_30: 68 Number of unique values with frequency less than 3 in id_30: 2 TRAIN Number of unique in id_31: 85 Number of unique values with frequency less than 3 in id_31: 4 TEST Number of unique in id_31: 85 Number of unique values with frequency less than 3 in id_31: 6 TRAIN Number of unique in id_32: 4 Number of unique values with frequency less than 3 in id_32: 0 TEST Number of unique in id_32: 4 Number of unique values with frequency less than 3 in id_32: 2 TRAIN Number of unique in id_34: 3 Number of unique values with frequency less than 3 in id_34: 0 TEST Number of unique in id_34: 3 Number of unique values with frequency less than 3 in id_34: 0 TRAIN Number of unique in id_36: 2 Number of unique values with frequency less than 3 in id_36: 0 TEST Number of unique in id_36: 2 Number of unique values with frequency less than 3 in id_36: 0 TRAIN Number of unique in id_37: 2 Number of unique values with frequency less than 3 in id_37: 0 TEST Number of unique in id_37: 2 Number of unique values with frequency less than 3 in id_37: 0 TRAIN Number of unique in id_38: 2 Number of unique values with frequency less than 3 in id_38: 0 TEST Number of unique in id_38: 2 Number of unique values with frequency less than 3 in id_38: 0
# low frequency cats will change into nans
columns_to_process = [i for i in ['id_12', 'id_13', 'id_14',
'id_15', 'id_17', 'id_19', 'id_30', 'id_31', 'id_32', 'id_34', 'id_36', 'id_37', 'id_38']]
for col in columns_to_process:
for df in [train, test]:
df[col] = df[col].apply(lambda x: np.nan if x in rareIds else x)
id_columns = train.loc[:, 'id_12':'id_38']
# Create an empty DataFrame to store the results
cramers_v_matrix = pd.DataFrame(index=id_columns.columns, columns=id_columns.columns, dtype=float)
# Fill in the Cramers V values for each pair of columns
for col1 in id_columns.columns:
for col2 in id_columns.columns:
cramers_v_matrix.loc[col1, col2] = cramers_v(id_columns[col1], id_columns[col2])
# Heatmap
plt.figure(figsize=(12, 10))
sns.heatmap(cramers_v_matrix, cmap='RdBu_r', annot=True, fmt=".2f", linewidths=.5)
plt.title('Cramers V Matrix Heatmap')
plt.show()
# We want to remove collinear features in 'id_' columns with a threshold of 0.75
id_columns = [col for col in train.columns if re.search('^id_(12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|32|33|34|35|36|37|38)', col)]
drop_columns = identify_collinear_categorical_features(train, id_columns, threshold=0.75)
# Display the columns to drop
print("Columns to drop:", drop_columns)
Columns to drop: ['id_29', 'id_20', 'id_28', 'id_16', 'id_37', 'id_33', 'id_35']
# Remove the collinear features
train = remove_collinear_categorical_features(train, drop_columns)
test = remove_collinear_categorical_features(test, drop_columns)
# Select only the columns that start with 'id_' and end with a number between 12 and 38
remaining_features = [col for col in train.columns if col.startswith('id_') and col[3:].isdigit() and 12 <= int(col[3:]) <= 38 and col not in ['id_35', 'id_29', 'id_20', 'id_33', 'id_28', 'id_16']]
for col in remaining_features:
distinct_count = train[col].nunique()
if distinct_count < 200:
# Target encode using target mean
temp_dict = train.groupby([col])['isFraud'].agg(['mean']).to_dict()['mean']
train[col + '_target_encoded'] = train[col].replace(temp_dict)
test[col + '_target_encoded'] = test[col].replace(temp_dict)
else:
# Frequency encode
train, test = frequency_encoding(train, test, columns=[col])
# Display the first few rows of the modified data
print(train.head())
print(test.head())
isFraud TransactionDT TransactionAmt dist1 C5 C13 \
TransactionID
2987000 0 86400 68.5 19.0 0.0 1.0
2987001 0 86401 29.0 NaN 0.0 1.0
2987002 0 86469 59.0 287.0 0.0 1.0
2987003 0 86499 50.0 NaN 0.0 25.0
2987004 0 86506 50.0 NaN 0.0 1.0
D1 D2 D3 D4 ... id_14_target_encoded \
TransactionID ...
2987000 14.0 NaN 13.0 NaN ... NaN
2987001 0.0 NaN NaN 0.0 ... NaN
2987002 0.0 NaN NaN 0.0 ... NaN
2987003 112.0 112.0 0.0 94.0 ... NaN
2987004 0.0 NaN NaN NaN ... 0.032246
id_15_target_encoded id_17_target_encoded id_19_freq_encoded \
TransactionID
2987000 NaN NaN 0.766912
2987001 NaN NaN 0.766912
2987002 NaN NaN 0.766912
2987003 NaN NaN 0.766912
2987004 0.047129 0.04181 0.008663
id_30_target_encoded id_31_target_encoded \
TransactionID
2987000 NaN NaN
2987001 NaN NaN
2987002 NaN NaN
2987003 NaN NaN
2987004 0.053551 0.07109
id_32_target_encoded id_34_target_encoded \
TransactionID
2987000 NaN NaN
2987001 NaN NaN
2987002 NaN NaN
2987003 NaN NaN
2987004 0.06345 0.037746
id_36_target_encoded id_38_target_encoded
TransactionID
2987000 NaN NaN
2987001 NaN NaN
2987002 NaN NaN
2987003 NaN NaN
2987004 0.078535 0.060191
[5 rows x 142 columns]
isFraud TransactionDT TransactionAmt dist1 C5 C13 \
TransactionID
3429905 0 11246665 30.95 NaN 1.0 1.0
3429906 0 11246704 53.95 0.0 1.0 3.0
3429907 0 11246761 117.00 2.0 95.0 409.0
3429908 0 11246761 29.00 5.0 0.0 89.0
3429909 0 11247072 200.00 NaN 0.0 2.0
D1 D2 D3 D4 ... id_14_target_encoded \
TransactionID ...
3429905 0.0 NaN NaN NaN ... NaN
3429906 549.0 549.0 362.0 NaN ... NaN
3429907 178.0 178.0 15.0 559.0 ... NaN
3429908 163.0 163.0 0.0 163.0 ... NaN
3429909 0.0 NaN NaN 57.0 ... 0.032246
id_15_target_encoded id_17_target_encoded id_19_freq_encoded \
TransactionID
3429905 NaN NaN 0.766912
3429906 NaN NaN 0.766912
3429907 NaN NaN 0.766912
3429908 NaN NaN 0.766912
3429909 0.047129 0.04181 0.008663
id_30_target_encoded id_31_target_encoded \
TransactionID
3429905 NaN NaN
3429906 NaN NaN
3429907 NaN NaN
3429908 NaN NaN
3429909 0.078125 0.101573
id_32_target_encoded id_34_target_encoded \
TransactionID
3429905 NaN NaN
3429906 NaN NaN
3429907 NaN NaN
3429908 NaN NaN
3429909 0.06345 0.06236
id_36_target_encoded id_38_target_encoded
TransactionID
3429905 NaN NaN
3429906 NaN NaN
3429907 NaN NaN
3429908 NaN NaN
3429909 0.078535 0.093292
[5 rows x 142 columns]
# Drop the original columns
train.drop(remaining_features, axis=1, inplace=True)
test.drop(remaining_features, axis=1, inplace=True)
pd.DataFrame(train.dtypes).to_clipboard()
for df in [train, test]:
column_details(regex='DeviceType', df=df)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE DeviceType: object, 2, %74.62 ['desktop' 'mobile' nan] Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE DeviceType: object, 2, %80.75 ['desktop' 'mobile' nan]
plot_col('DeviceType', df=train)
fraud_rates_device_type = train.groupby('DeviceType')['isFraud'].mean().reset_index()
fraud_rates_device_type.rename(columns={'isFraud': 'FraudRate'}, inplace=True)
fraud_rates_device_type.sort_values(by='FraudRate', ascending=False, inplace=True)
print(fraud_rates_device_type)
DeviceType FraudRate 1 mobile 0.098887 0 desktop 0.061458
#target encoding
col = 'DeviceType'
temp_dict = train.groupby([col])['isFraud'].agg(['mean']).to_dict()['mean']
train[col+'_target_encoded'] = train[col].replace(temp_dict)
test[col+'_target_encoded'] = test[col].replace(temp_dict)
# Drop the original column
train.drop('DeviceType', axis=1, inplace=True)
test.drop('DeviceType', axis=1, inplace=True)
for df in [train, test]:
column_details(regex='DeviceInfo', df=df)
Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE DeviceInfo: object, 1583, %78.49 ['0PAJ5' '0PJA2' '0PM92' ... 'verykools5035' 'xs-Z47b7VqTMxs' nan] Unique Values of the Features: feature: DTYPE, NUNIQUE, NULL_RATE DeviceInfo: object, 962, %84.16 ['2PYB2' '4009F' '4013M Build/KOT49H' '4047A Build/NRD90M' '4047G Build/NRD90M' '47418' '5010G Build/MRA58K' '5010S Build/MRA58K' '5011A Build/NRD90M' '5012G Build/MRA58K' '5015A Build/LMY47I' '5025G Build/LMY47I' '5044A' '5049W Build/NRD90M' '5051A Build/MMB29M' '5054S Build/LMY47V' '5056A Build/MMB29M' '5056N' '5080A Build/MRA58K' '5085B Build/MRA58K' '6037B' '6039A Build/LRX22G' '6045I Build/LRX22G' '6055B' '7048A Build/LRX22G' '7_Plus' '8050G Build/LMY47I' '8080 Build/LRX21M' '9008A Build/NRD90M' 'A0001' 'A3-A20' 'A463BG' 'A574BL Build/NMF26F' 'A577VL' 'AERIAL' 'AKUS' 'ALCATEL' 'ALCATEL ONE TOUCH 7047A Build/JDQ39' 'ALE-L21 Build/HuaweiALE-L21' 'ALE-L23 Build/HuaweiALE-L23' 'ALP-L09 Build/HUAWEIALP-L09' 'ALP-L09 Build/HUAWEIALP-L09S' 'AM508' 'ANE-LX3 Build/HUAWEIANE-LX3' 'ASUS_A001' 'ASUS_X008D Build/NRD90M' 'ASUS_X00HD Build/NMF26F' 'ASUS_X00ID' 'ASUS_X018D' 'ASUS_Z00AD Build/LRX21V' 'ASUS_Z017D' 'ASUS_Z01BDC' 'AX1060' 'AX1070' 'AX820 Build/MRA58K' 'AX821 Build/MRA58K' 'AX921 Build/MRA58K' 'Alcatel' 'Alcatel_4060O Build/MMB29M' 'Alcatel_5044R Build/NRD90M' 'Alcatel_5098O Build/MMB29M' 'Alumini3 Build/MRA58K' 'Alumini3Plus' 'Android 4.4.2' 'Android 5.1' 'Android 5.1.1' 'Android 6.0' 'Android 6.0.1' 'Android 7.0' 'Android 7.1.2' 'Aquaris' 'Aquaris V Build/N2G47H' 'Aquaris X Build/NMF26F' 'Aquaris X5 Plus Build/NMF26F' 'Aquaris_A4.5' 'Archos' 'Azumi_KINZO_A5_QL' 'B1-750' 'BAC-L03 Build/HUAWEIBAC-L03' 'BBB100-1' 'BLA-L29 Build/HUAWEIBLA-L29' 'BLADE A520 Build/NRD90M' 'BLADE A602 Build/MRA58K' 'BLADE L7 Build/MRA58K' 'BLADE V7 Build/MRA58K' 'BLADE V8 Build/NRD90M' 'BLADE V8 SE Build/NRD90M' 'BLADE V8Q Build/N2G47H' 'BLL-L23 Build/HUAWEIBLL-L23' 'BLN-L21 Build/HONORBLN-L21' 'BLU' 'BLU ENERGY X PLUS Build/LRX21M' 'BLU LIFE XL Build/L050U' 'BND-L34' 'BV8000Pro' 'Blade A465 Build/LMY47D' 'Blade A510 Build/MRA58K' 'Blade L2 Plus Build/KOT49H' 'Blade L5 Build/LMY47I' 'Blade V580 Build/LMY47D' 'Blade V6 Build/LRX22G' 'Blade V6 Max Build/MRA58K' 'Blade V6 Plus Build/MRA58K' 'Bolt' 'Build/KOT49H' 'Build/OPM1.171019.011' 'Build/OPR1.170623.032' 'Build/OPR6.170623.013' 'C6603' 'C6903' 'C6906 Build/14.6.A.1.236' 'CAM-L03 Build/HUAWEICAM-L03' 'CHC-U03 Build/HuaweiCHC-U03' 'CLT-L09' 'CPH1607' 'CPH1723' 'CRO-L03 Build/HUAWEICRO-L03' 'D2306' 'D2306 Build/18.6.A.0.182' 'D2406' 'D5306 Build/19.4.A.0.182' 'D5316 Build/19.4.A.0.182' 'D5503' 'D6503' 'D6603 Build/23.5.A.1.291' 'DASH' 'DLI-L22 Build/HONORDLI-L22' 'DOMOS' 'Dream' 'E2104 Build/24.0.A.5.14' 'E2306 Build/26.1.A.3.111' 'E2306 Build/26.3.A.1.33' 'E501' 'E5306' 'E5306 Build/27.3.A.0.129' 'E5506' 'E5506 Build/29.1.A.0.101' 'E5506 Build/29.2.A.0.166' 'E5606 Build/30.2.A.1.21' 'E5823' 'E5823 Build/32.4.A.1.54' 'E6553' 'E6603 Build/32.4.A.1.54' 'E6683' 'E6790TM' 'E6833' 'E6853 Build/32.4.A.1.54' 'EGO' 'EML-L29 Build/HUAWEIEML-L29' 'EVA-L09 Build/HUAWEIEVA-L09' 'EVA-L19 Build/HUAWEIEVA-L19' 'Energy X 2 Build/E050L' 'F3111 Build/33.3.A.1.115' 'F3111 Build/33.3.A.1.97' 'F3113' 'F3113 Build/33.3.A.1.97' 'F3213 Build/36.0.A.2.146' 'F3213 Build/36.1.A.1.86' 'F3311' 'F3313 Build/37.0.A.2.108' 'F3313 Build/37.0.A.2.248' 'F5121' 'F5121 Build/34.3.A.0.252' 'F5121 Build/34.4.A.2.19' 'F5122' 'F5321' "F80'S+" 'F8331' 'F8332' 'FEVER' 'FIG-LX3 Build/HUAWEIFIG-LX3' 'FP2' 'FRD-L09 Build/HUAWEIFRD-L09' 'Fusion5_u7' 'G3123' 'G3123 Build/40.0.A.6.175' 'G3123 Build/40.0.A.6.189' 'G3223' 'G3223 Build/42.0.A.4.101' 'G3223 Build/42.0.A.4.167' 'G3313 Build/43.0.A.5.79' 'G3313 Build/43.0.A.7.25' 'G3423' 'G630-U251' 'G8141' 'G8142' 'GRANT' 'GT-I8190L Build/JZO54K' 'GT-I8190N' 'GT-I9060L Build/JDQ39' 'GT-I9060M Build/KTU84P' 'GT-I9195L' 'GT-I9505 Build/LRX22C' 'GT-I9506' 'GT-I9515' 'GT-N5110 Build/JDQ39' 'GT-N8010' 'GT-P5210 Build/JDQ39' 'GT-P5210 Build/KOT49H' 'GT-S7580L Build/JDQ39' 'GT-S7582' 'Grand' 'Gravity Build/NRD90M' 'H3321' 'HTC' 'HTC Desire 10 lifestyle Build/MMB29M' 'HTC Desire 526G Build/KOT49H' 'HTC Desire 530 Build/MMB29M' 'HTC Desire 626s Build/LMY47O' 'HTC Desire 650 Build/MMB29M' 'HTC One A9 Build/NRD90M' 'HTC One A9s Build/MRA58K' 'HTC One M9 Build/NRD90M' 'HTC One mini Build/KOT49H' 'HTC One_M8 Build/MRA58K' 'HTC U11 Build/NMF26X' 'HTC6535LVW' 'HTC6545LVW' 'HTC_Desire_820' 'HTC_One' 'HTC_One_M8/4.28.502.2' 'HUAWEI' 'HUAWEI Build/MMB28B' 'HUAWEI CAN-L11 Build/HUAWEICAN-L11' 'HUAWEI CUN-L03 Build/HUAWEICUN-L03' 'HUAWEI G7-L03 Build/HuaweiG7-L03' 'HUAWEI NXT-L09 Build/HUAWEINXT-L09' 'HUAWEI RIO-L03 Build/HUAWEIRIO-L03' 'HUAWEI TAG-L13 Build/HUAWEITAG-L13' 'HUAWEI VNS-L21 Build/HUAWEIVNS-L21' 'HUAWEI VNS-L23 Build/HUAWEIVNS-L23' 'HUAWEI VNS-L31 Build/HUAWEIVNS-L31' 'HUAWEI VNS-L53 Build/HUAWEIVNS-L53' 'HUAWEI Y360-U23 Build/HUAWEIY360-U23' 'HUAWEI Y520-U03 Build/HUAWEIY520-U03' 'HUAWEI Y560-L03 Build/HUAWEIY560-L03' 'Hisense E51 Build/LMY47V' 'Hisense F102 Build/NRD90M' 'Hisense F20 Build/MMB29M' 'Hisense F23 Build/NRD90M' 'Hisense F24 Build/NRD90M' 'Hisense F32 Build/NMF26F' 'Hisense F8 MINI Build/NRD90M' 'Hisense L675 Build/MRA58K' 'Hisense L675 PRO Build/NRD90M' 'Hisense T963 Build/MRA58K' 'Hisense U963 Build/MRA58K' 'ILIUM' 'IO' 'Ilium' 'Ilium L1000 Build/LRX22G' 'Ilium L1120 Build/NRD90M' 'Ilium L610 Build/MRA58K' 'Ilium L910 Build/MRA58K' 'Ilium LT500 Build/LMY47O' 'Ilium LT510 Build/MRA58K' 'Ilium Pad T7X Build/LMY47I' 'Ilium X210 Build/LMY47I' 'Ilium X220 Build/MRA58K' 'Ilium X510 Build/MRA58K' 'Ilium X520 Build/NRD90M' 'Ilium X710 Build/MRA58K' 'KFDOWI Build/LVY48F' 'KFFOWI Build/LVY48F' 'KFGIWI Build/LVY48F' 'KFSAWI' 'KFSUWI Build/LVY48F' 'KFTBWI Build/LVY48F' 'Kylin' 'LDN-LX3 Build/HUAWEILDN-LX3' 'LG-D320 Build/KOT49I.V10a' 'LG-D331 Build/LRX22G' 'LG-D373' 'LG-D373 Build/KOT49I.V10a' 'LG-D680 Build/KOT49I' 'LG-D693n Build/LRX22G' 'LG-D722' 'LG-D850' 'LG-D855' 'LG-D855 Build/LRX21R' 'LG-E975' 'LG-H320 Build/LRX21Y' 'LG-H420 Build/LRX21Y' 'LG-H500 Build/LRX21Y' 'LG-H540' 'LG-H542 Build/LRX22G' 'LG-H542 Build/MRA58K' 'LG-H650 Build/LMY47V' 'LG-H650 Build/MRA58K' 'LG-H700 Build/NRD90U' 'LG-H811 Build/MRA58K' 'LG-H815 Build/MRA58K' 'LG-H820' 'LG-H830' 'LG-H840 Build/MMB29M' 'LG-H840 Build/NRD90U' 'LG-H870 Build/NRD90U' 'LG-H872 Build/NRD90U' 'LG-H910 Build/NRD90M' 'LG-H918 Build/NRD90M' 'LG-H932' 'LG-H933' 'LG-H990 Build/NRD90M' 'LG-K200 Build/MXB48T' 'LG-K212' 'LG-K220 Build/MXB48T' 'LG-K240 Build/MXB48T' 'LG-K350' 'LG-K371' 'LG-K410 Build/LRX22G' 'LG-K428 Build/MMB29M' 'LG-K430 Build/MRA58K' 'LG-K450 Build/MXB48T' 'LG-K500 Build/MMB29M' 'LG-K530 Build/MMB29M' 'LG-K530 Build/NRD90U' 'LG-K580 Build/MRA58K' 'LG-LG870' 'LG-LS777 Build/NRD90U' 'LG-LS993 Build/NRD90U' 'LG-LS997 Build/NRD90M' 'LG-M150' 'LG-M153 Build/MXB48T' 'LG-M200 Build/NRD90U' 'LG-M210 Build/NRD90U' 'LG-M250 Build/NRD90U' 'LG-M257' 'LG-M320 Build/NRD90U' 'LG-M400 Build/NRD90U' 'LG-M700 Build/NMF26X' 'LG-M710 Build/NRD90U' 'LG-P714' 'LG-SP320' 'LG-TP260 Build/NRD90U' 'LG-TP450 Build/NRD90U' 'LG-V410/V41020c' 'LG-X165g Build/LRX21M' 'LG-X180g Build/LMY47I' 'LG-X210 Build/LMY47I' 'LG-X220 Build/LMY47I' 'LG-X230 Build/MRA58K' 'LG-X240 Build/MRA58K' 'LGL163BL' 'LGL164VL Build/NRD90U' 'LGL83BL' 'LGLS676 Build/MXB48T' 'LGLS770' 'LGLS775 Build/NRD90U' 'LGLS990' 'LGLS992' 'LGMP260 Build/NRD90U' 'LGMP450 Build/NRD90U' 'LGMS210 Build/NRD90U' 'LGMS330 Build/LMY47V' 'LGMS395' 'LGMS428' 'LGMS550 Build/MXB48T' 'LGMS550 Build/NRD90U' 'LGMS631' 'LGMS631 Build/MRA58K' 'LIMIT' 'LM-X210(G' 'LS5' 'Lenovo' 'Lenovo A2016b30 Build/MRA58K' 'Lenovo A6020l37 Build/LMY47V' 'Lenovo K33b36 Build/MMB29M' 'Lenovo K33b36 Build/NRD90N' 'Lenovo PB1-750M Build/S100' 'Lenovo PB2-650Y Build/MRA58K' 'Lenovo PB2-670Y Build/MRA58K' 'Lenovo TB3-710F Build/LRX21M' 'Lenovo TB3-710I Build/LMY47I' 'Lenovo YT3-850F Build/MMB29M' 'Lenovo YT3-850M Build/MMB29M' 'Lenovo-A6020l36 Build/LMY47V' 'LenovoA3300-GV Build/JDQ39' 'Linux i686' 'Linux x86_64' 'M4' 'M4 SS4451 Build/LMY47D' 'M4 SS4453 Build/MMB29M' 'M4 SS4456 Build/LMY47V' 'M4 SS4457 Build/MRA58K' 'M4 SS4457-R Build/NRD90M' 'M4 SS4458 Build/MMB29M' 'MALC' 'MHA-L09 Build/HUAWEIMHA-L09' 'MHA-L29 Build/HUAWEIMHA-L29' 'MOT-A6020l37 Build/LMY47V' 'MTT' 'MYA-L03 Build/HUAWEIMYA-L03' 'MYA-L13 Build/HUAWEIMYA-L13' 'MacOS' 'Mi A1 Build/N2G47H' 'Mi A1 Build/OPR1.170623.026' 'Microsoft' 'Moto' 'Moto C Build/NRD90M.054' 'Moto C Build/NRD90M.057' 'Moto C Build/NRD90M.063' 'Moto C Build/NRD90M.070' 'Moto C Plus Build/NRD90M.05.034' 'Moto E (4) Build/NDQS26.69-23-2-3' 'Moto E (4) Build/NDQS26.69-64-2' 'Moto E (4) Build/NMA26.42-19' 'Moto E (4) Build/NMA26.42-69' 'Moto E (4) Plus Build/NMA26.42-142' 'Moto E (4) Plus Build/NMA26.42-152' 'Moto E (4) Plus Build/NMA26.42-69' 'Moto G (4) Build/NPJ25.93-14' 'Moto G (4) Build/NPJ25.93-14.5' 'Moto G (4) Build/NPJ25.93-14.7' 'Moto G (4) Build/NPJS25.93-14-10' 'Moto G (4) Build/NPJS25.93-14-13' 'Moto G (4) Build/NPJS25.93-14-15' 'Moto G (4) Build/NPJS25.93-14-18' 'Moto G (4) Build/NPJS25.93-14-8' 'Moto G (5) Build/NPP25.137-33' 'Moto G (5) Build/NPP25.137-38' 'Moto G (5) Build/NPP25.137-72' 'Moto G (5) Build/NPP25.137-82' 'Moto G (5) Build/NPP25.137-93' 'Moto G (5) Build/NPPS25.137-15-11' 'Moto G (5) Build/NPPS25.137-93-4' 'Moto G (5) Build/NPPS25.137-93-8' 'Moto G (5) Plus Build/NPN25.137-72' 'Moto G (5) Plus Build/NPN25.137-82' 'Moto G (5) Plus Build/NPN25.137-92' 'Moto G (5) Plus Build/NPNS25.137-15-11' 'Moto G (5) Plus Build/NPNS25.137-92-10' 'Moto G (5) Plus Build/NPNS25.137-92-4' 'Moto G (5) Plus Build/NPNS25.137-92-8' 'Moto G (5) Plus Build/NPNS25.137-93-8' 'Moto G (5S' 'Moto G Play Build/MPIS24.241-15.3-26' 'Moto G Play Build/MPIS24.241-15.3-7' 'Moto G Play Build/NPI26.48-36' 'Moto G Play Build/NPIS26.48-36-2' 'Moto G Play Build/NPIS26.48-36-5' 'Moto X Play Build/NPD26.48-24-1' 'Moto Z (2' 'Moto Z2 Play Build/NPS26.118-19' 'Moto Z2 Play Build/NPSS26.118-19-11' 'Moto Z2 Play Build/NPSS26.118-19-14' 'Moto Z2 Play Build/NPSS26.118-19-4' 'MotoE2 Build/LPCS23.13-56-5' 'MotoE2(4G-LTE' 'MotoG3 Build/MPI24.65-25' 'MotoG3 Build/MPI24.65-33.1-2' 'MotoG3 Build/MPIS24.107-55-2-17' 'MotoG3 Build/MPIS24.65-33.1-2-16' 'MotoG3-TE Build/MPD24.65-33' 'MotoG3-TE Build/MPDS24.65-33-1-3' 'MotoG3-TE Build/MPDS24.65-33-1-30' 'N9560 Build/NMF26F' 'NEM-L51 Build/HONORNEM-L51' 'Nexus' 'Nexus 5 Build/M4B30Z' 'Nexus 5X Build/OPR4.170623.006' 'Northwell' 'ONE' 'ONE A2003 Build/MMB29M' 'ONE TOUCH 4016A Build/JDQ39' 'ONE TOUCH 4033A Build/JDQ39' 'ONEPLUS A5010 Build/OPM1.171019.011' 'ORION' 'P008 Build/NRD90M' 'P4526A Build/NRD90M' 'P5006A Build/NRD90M' 'P5026A' 'P5046A' 'PRA-LX1 Build/HUAWEIPRA-LX1' 'PRA-LX3 Build/HUAWEIPRA-LX3' 'PSPC550 Build/LMY47D' 'PULP 4G Build/LMY47V' 'Pixel' 'Pixel 2 XL Build/OPM2.171019.029' 'Pixel 2 XL Build/OPM2.171019.029.B1' 'Pixel Build/OPM2.171019.029' 'Pixel Build/OPM4.171019.016.B1' 'Pixel XL Build/OPM4.171019.016.B1' 'QTAQZ3 Build/LMY47V' 'QTASUN1 Build/NRD90M' 'R2' 'R831L' 'RCT6203W46 Build/KOT49H' 'RCT6303W87M7 Build/MRA58K' 'RCT6513W87 Build/MRA58K' 'REVVLPLUS' 'RNE-L03 Build/HUAWEIRNE-L03' 'RNE-L21 Build/HUAWEIRNE-L21' 'RNE-L22 Build/HUAWEIRNE-L22' 'RNE-L23 Build/HUAWEIRNE-L23' 'RS988' 'Redmi' 'Redmi 3S Build/MMB29M' 'Redmi 4A Build/MMB29M' 'Redmi 4A Build/N2G47H' 'Redmi 4X Build/N2G47H' 'Redmi 5 Plus Build/N2G47H' 'Redmi Note 3 Build/MMB29M' 'Redmi Note 4 Build/NRD90M' 'Redmi Note 4X Build/MRA58K' 'Redmi Note 5A Build/N2G47H' 'S.N.O.W.4' 'S471' 'S57 Build/KTU84P' 'SAMSUNG' 'SAMSUNG SM-A300H Build/LRX22G' 'SAMSUNG SM-A310F Build/NRD90M' 'SAMSUNG SM-A310F/A310FXXU4CRB1 Build/NRD90M' 'SAMSUNG SM-A320FL Build/NRD90M' 'SAMSUNG SM-A320Y Build/NRD90M' 'SAMSUNG SM-A510F Build/NRD90M' 'SAMSUNG SM-A510M Build/NRD90M' 'SAMSUNG SM-A520F Build/NRD90M' 'SAMSUNG SM-A710M Build/NRD90M' 'SAMSUNG SM-A720F Build/NRD90M' 'SAMSUNG SM-A730F Build/NMF26X' 'SAMSUNG SM-G531H Build/LMY48B' 'SAMSUNG SM-G532M Build/MMB29T' 'SAMSUNG SM-G550T1 Build/MMB29K' 'SAMSUNG SM-G570M Build/MMB29K' 'SAMSUNG SM-G570M Build/NRD90M' 'SAMSUNG SM-G610M Build/MMB29K' 'SAMSUNG SM-G610M Build/NRD90M' 'SAMSUNG SM-G890A Build/NRD90M' 'SAMSUNG SM-G892A Build/R16NW' 'SAMSUNG SM-G900A Build/MMB29M' 'SAMSUNG SM-G900F Build/MMB29M' 'SAMSUNG SM-G900I Build/MMB29M' 'SAMSUNG SM-G900M Build/LRX21T' 'SAMSUNG SM-G920A Build/NRD90M' 'SAMSUNG SM-G920F Build/NRD90M' 'SAMSUNG SM-G920I Build/NRD90M' 'SAMSUNG SM-G920T Build/NRD90M' 'SAMSUNG SM-G925F Build/NRD90M' 'SAMSUNG SM-G925I Build/NRD90M' 'SAMSUNG SM-G928G Build/NRD90M' 'SAMSUNG SM-G930A Build/NRD90M' 'SAMSUNG SM-G930F Build/NRD90M' 'SAMSUNG SM-G930F/XXU2DRC1 Build/NRD90M' 'SAMSUNG SM-G930P Build/NRD90M' 'SAMSUNG SM-G930R4 Build/NRD90M' 'SAMSUNG SM-G930T Build/NRD90M' 'SAMSUNG SM-G935A Build/NRD90M' 'SAMSUNG SM-G935F Build/NRD90M' 'SAMSUNG SM-G935P Build/NRD90M' 'SAMSUNG SM-G935T Build/NRD90M' 'SAMSUNG SM-G935W8 Build/NRD90M' 'SAMSUNG SM-G950F Build/NRD90M' 'SAMSUNG SM-G950F Build/R16NW' 'SAMSUNG SM-G950U Build/NRD90M' 'SAMSUNG SM-G950U Build/R16NW' 'SAMSUNG SM-G950U1 Build/R16NW' 'SAMSUNG SM-G955F Build/NRD90M' 'SAMSUNG SM-G955F Build/R16NW' 'SAMSUNG SM-G955U Build/NRD90M' 'SAMSUNG SM-G955U Build/R16NW' 'SAMSUNG SM-G9600 Build/R16NW' 'SAMSUNG SM-G960U Build/R16NW' 'SAMSUNG SM-G9650 Build/R16NW' 'SAMSUNG SM-G965F Build/R16NW' 'SAMSUNG SM-G965U Build/R16NW' 'SAMSUNG SM-G965U1 Build/R16NW' 'SAMSUNG SM-J120H Build/LMY47V' 'SAMSUNG SM-J250M Build/NMF26X' 'SAMSUNG SM-J320FN Build/LMY47V' 'SAMSUNG SM-J320M Build/LMY47V' 'SAMSUNG SM-J327A Build/NRD90M' 'SAMSUNG SM-J327AZ Build/NRD90M' 'SAMSUNG SM-J327T1 Build/NRD90M' 'SAMSUNG SM-J500FN Build/MMB29M' 'SAMSUNG SM-J500M Build/MMB29M' 'SAMSUNG SM-J510MN Build/MMB29M' 'SAMSUNG SM-J530F Build/NRD90M' 'SAMSUNG SM-J700M Build/LMY48B' 'SAMSUNG SM-J700M Build/MMB29K' 'SAMSUNG SM-J701M Build/NRD90M' 'SAMSUNG SM-J710MN Build/MMB29K' 'SAMSUNG SM-J710MN Build/NRD90M' 'SAMSUNG SM-J727T1 Build/NRD90M' 'SAMSUNG SM-J730GM Build/NRD90M' 'SAMSUNG SM-N910V Build/MMB29M' 'SAMSUNG SM-N920T Build/NRD90M' 'SAMSUNG SM-N950F Build/NMF26X' 'SAMSUNG SM-N950U Build/NMF26X' 'SAMSUNG SM-N950U Build/R16NW' 'SAMSUNG SM-N950U1 Build/R16NW' 'SAMSUNG SM-P580 Build/NRD90M' 'SAMSUNG SM-T530NU Build/LRX22G' 'SAMSUNG SM-T580 Build/NRD90M' 'SAMSUNG SM-T587P Build/NRD90M' 'SAMSUNG SM-T810 Build/NRD90M' 'SAMSUNG-SM-G530AZ Build/LMY48B' 'SAMSUNG-SM-G890A Build/MMB29K' 'SAMSUNG-SM-G890A Build/NRD90M' 'SAMSUNG-SM-G891A Build/NRD90M' 'SAMSUNG-SM-G900A Build/KOT49H' 'SAMSUNG-SM-G900A Build/MMB29M' 'SAMSUNG-SM-G920A Build/NRD90M' 'SAMSUNG-SM-G920AZ' 'SAMSUNG-SM-G925A Build/MMB29K' 'SAMSUNG-SM-G928A Build/NRD90M' 'SAMSUNG-SM-G930A Build/NRD90M' 'SAMSUNG-SM-G935A' 'SAMSUNG-SM-G935A Build/NRD90M' 'SAMSUNG-SM-J320A Build/MMB29K' 'SAMSUNG-SM-J320A Build/NMF26X' 'SAMSUNG-SM-J320AZ Build/MMB29K' 'SAMSUNG-SM-J327A Build/NRD90M' 'SAMSUNG-SM-J727AZ' 'SAMSUNG-SM-N900A Build/LRX21V' 'SAMSUNG-SM-N910A' 'SAMSUNG-SM-N920A Build/NRD90M' 'SAMSUNG-SM-T377A Build/MMB29K' 'SGH-I747M' 'SGH-M919 Build/KTU84P' 'SGH-M919V' 'SGP511' 'SGP611' 'SLA-L03 Build/HUAWEISLA-L03' 'SM-A300H Build/LRX22G' 'SM-A300M 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'SM-G900F Build/MMB29M' 'SM-G900FD' 'SM-G900H Build/MMB29K' 'SM-G900M Build/KOT49H' 'SM-G900M Build/LRX21T' 'SM-G900M Build/MMB29M' 'SM-G900P Build/MMB29M' 'SM-G900T Build/MMB29M' 'SM-G900T1' 'SM-G900V Build/MMB29M' 'SM-G901F' 'SM-G903F Build/MMB29K' 'SM-G920F Build/NRD90M' 'SM-G920I Build/NRD90M' 'SM-G920P' 'SM-G920P Build/NRD90M' 'SM-G920T' 'SM-G920T Build/NRD90M' 'SM-G920V Build/MMB29K' 'SM-G920V Build/NRD90M' 'SM-G920W8' 'SM-G925F Build/NRD90M' 'SM-G925I Build/MMB29K' 'SM-G925I Build/NRD90M' 'SM-G925R4' 'SM-G925T Build/NRD90M' 'SM-G928G Build/MMB29K' 'SM-G928G Build/NRD90M' 'SM-G928T Build/NRD90M' 'SM-G928V Build/NRD90M' 'SM-G930F Build/MMB29K' 'SM-G930F Build/NRD90M' 'SM-G930P' 'SM-G930P Build/NRD90M' 'SM-G930R4 Build/NRD90M' 'SM-G930T Build/NRD90M' 'SM-G930U Build/NRD90M' 'SM-G930V Build/MMB29M' 'SM-G930V Build/NRD90M' 'SM-G930VL Build/NRD90M' 'SM-G930W8 Build/NRD90M' 'SM-G935F Build/MMB29K' 'SM-G935F Build/NRD90M' 'SM-G935P Build/NRD90M' 'SM-G935T Build/NRD90M' 'SM-G935V Build/NRD90M' 'SM-G950F Build/NRD90M' 'SM-G950F Build/R16NW' 'SM-G950U Build/NRD90M' 'SM-G950U Build/R16NW' 'SM-G950U1 Build/R16NW' 'SM-G950W' 'SM-G955F Build/NRD90M' 'SM-G955F Build/R16NW' 'SM-G955U Build/NRD90M' 'SM-G955U Build/R16NW' 'SM-G955U1 Build/R16NW' 'SM-G955W' 'SM-G9600 Build/R16NW' 'SM-G960F Build/R16NW' 'SM-G960U Build/R16NW' 'SM-G9650 Build/R16NW' 'SM-G965F Build/R16NW' 'SM-G965U Build/R16NW' 'SM-J105B Build/LMY47V' 'SM-J110M Build/LMY48B' 'SM-J111M Build/LMY47V' 'SM-J120FN Build/LMY47X' 'SM-J120H Build/LMY47V' 'SM-J120M' 'SM-J120W' 'SM-J200H' 'SM-J200M Build/LMY47X' 'SM-J250M Build/NMF26X' 'SM-J320FN Build/LMY47V' 'SM-J320M Build/LMY47V' 'SM-J320V Build/MMB29M' 'SM-J320V Build/NMF26X' 'SM-J327P Build/MMB29M' 'SM-J327T1 Build/NRD90M' 'SM-J327VPP Build/NRD90M' 'SM-J500H' 'SM-J500M Build/LMY48B' 'SM-J500M Build/MMB29M' 'SM-J510MN Build/MMB29M' 'SM-J510MN Build/NMF26X' 'SM-J530F Build/NRD90M' 'SM-J530GM Build/NRD90M' 'SM-J700F' 'SM-J700M Build/LMY48B' 'SM-J700M Build/MMB29K' 'SM-J700T Build/MMB29K' 'SM-J700T Build/NMF26X' 'SM-J700T1' 'SM-J700T1 Build/NMF26X' 'SM-J701M Build/NRD90M' 'SM-J7108' 'SM-J710F Build/NRD90M' 'SM-J710MN Build/MMB29K' 'SM-J710MN Build/NRD90M' 'SM-J727T Build/NRD90M' 'SM-J727T1 Build/NRD90M' 'SM-J727U Build/NRD90M' 'SM-J727V Build/NRD90M' 'SM-J727VPP Build/NRD90M' 'SM-J730GM Build/NRD90M' 'SM-N9005 Build/LRX21V' 'SM-N910C Build/MMB29K' 'SM-N910F Build/MMB29M' 'SM-N910T Build/LMY47X' 'SM-N910V Build/MMB29M' 'SM-N920G Build/NRD90M' 'SM-N920P Build/NRD90M' 'SM-N920T Build/NRD90M' 'SM-N920V' 'SM-N920V Build/NRD90M' 'SM-N950F Build/NMF26X' 'SM-N950F Build/R16NW' 'SM-N950U Build/NMF26X' 'SM-N950U Build/R16NW' 'SM-N950U1 Build/R16NW' 'SM-P350 Build/MMB29M' 'SM-P355M' 'SM-P355M Build/MMB29M' 'SM-P550' 'SM-P550 Build/NMF26X' 'SM-P580 Build/NRD90M' 'SM-P600 Build/LMY47X' 'SM-P605V' 'SM-P607T' 'SM-S727VL Build/MMB29M' 'SM-S902L' 'SM-T110 Build/JDQ39' 'SM-T113 Build/KTU84P' 'SM-T113NU Build/KTU84P' 'SM-T230NU Build/KOT49H' 'SM-T280 Build/LMY47V' 'SM-T310 Build/KOT49H' 'SM-T330' 'SM-T330NU Build/LMY47X' 'SM-T350 Build/MMB29M' 'SM-T350 Build/NMF26X' 'SM-T377P Build/NMF26X' 'SM-T377V Build/NMF26X' 'SM-T530 Build/LRX22G' 'SM-T530NU Build/LRX22G' 'SM-T550 Build/NMF26X' 'SM-T555' 'SM-T560 Build/KTU84P' 'SM-T560NU Build/MMB29M' 'SM-T560NU Build/NMF26X' 'SM-T580 Build/NRD90M' 'SM-T670' 'SM-T700 Build/MMB29K' 'SM-T710 Build/NRD90M' 'SM-T713 Build/NRD90M' 'SM-T800 Build/MMB29K' 'SM-T810' 'SM-T810 Build/NRD90M' 'SM-T813 Build/NRD90M' 'SM-T817V Build/NRD90M' 'SM-T820 Build/NRD90M' 'SM-T825 Build/NRD90M' 'SM-T827V Build/NRD90M' 'SMART' 'SP7731G' 'STF-L09 Build/HUAWEISTF-L09' 'STK_Sync_5e' 'STUDIO_G_HD' 'Shift Build/LMY47I' 'T08' 'TA-1025 Build/OPR1.170623.026' 'TA-1027 Build/OPR1.170623.026' 'TA-1028 Build/NMF26O' 'TA-1028 Build/NRD90M' 'TA-1028 Build/O00623' 'TA-1032' 'TA-1038 Build/NMF26O' 'TA-1038 Build/O00623' 'TA-1044 Build/NMF26F' 'TA-1044 Build/OPR1.170623.026' 'TOMMY2' 'TREKKER-X3 Build/MMB29M' 'TRT-L53 Build/HUAWEITRT-L53' 'Techpad' 'Trident/7.0' 'UL40' 'VK700 Build/LRX22G' 'VKY-L09 Build/HUAWEIVKY-L09' 'VS425' 'VS425PP Build/LMY47V' 'VS501 Build/NRD90U' 'VS986 Build/MRA58K' 'VS988 Build/NRD90U' 'VS995 Build/NRD90M' 'VTR-L09 Build/HUAWEIVTR-L09' 'VTR-L29' 'WAS-LX1 Build/HUAWEIWAS-LX1' 'WAS-LX1A Build/HUAWEIWAS-LX1A' 'WAS-LX2J' 'WAS-LX3 Build/HUAWEIWAS-LX3' 'Windows' 'Windows NT 6.1' 'Windows NT 6.2' 'X78' 'XT1003' 'XT1008 Build/LPBS23.13-56-2' 'XT1021 Build/KXC21.5-40' 'XT1032' 'XT1032 Build/LPBS23.13-56-2' 'XT1040' 'XT1053 Build/LPAS23.12-21.7-1' 'XT1058 Build/LPAS23.12-21.7-1' 'XT1063 Build/MPB24.65-34' 'XT1063 Build/MPB24.65-34-3' 'XT1064 Build/MPB24.65-34-3' 'XT1080 Build/SU6-7.7' 'XT1096' 'XT1097' 'XT1225' 'XT1254 Build/MCG24.251-5-5' 'XT1563 Build/MPD24.65-25' 'XT1572 Build/NPHS25.200-15-8' 'XT1580 Build/NPKS25.200-17-8' 'XT1585 Build/NCK25.118-10.5' 'XT1609 Build/NPIS26.48-38-3' 'XT1635-01' 'XT1635-02' 'XT1635-02 Build/NPN26.118-22-2' 'XT1635-02 Build/OPN27.76-12-22' 'XT1650' 'XT1650 Build/NCLS26.118-23-13-6-5' 'XT1650 Build/NPLS26.118-20-5-3' 'XT1710-02 Build/NDSS26.118-23-19-6' 'Y550-L02' 'Y635-L03 Build/HuaweiY635-L03' 'YOLO' 'Z' 'Z557BL' 'Z812' 'Z833' 'Z835 Build/NMF26V' 'Z839' 'Z9 PLUS Build/NRD90M' 'Z955A' 'Z965 Build/NMF26V' 'Z981 Build/MMB29M' 'Z982 Build/NMF26V' 'Z983 Build/NMF26F' 'ZA509' 'ZA990' 'ZTE BLADE A6 Build/NMF26F' 'ZTE BLADE V8 MINI Build/NRD90M' 'es-us' 'gxq6580_weg_l Build/LMY47I' 'hi6210sft Build/MRA58K' 'iOS Device' 'iris 820 Build/MRA58K' 'iris 870 Build/MRA58K' 'iris50' 'moto' 'moto g(6) play Build/OPP27.61-14-4' 'moto x4 Build/NPW26.83-18-2-0-4' 'moto x4 Build/NPW26.83-42' 'moto x4 Build/OPW27.57-40' 'moto x4 Build/OPWS27.57-40-14' 'moto x4 Build/OPWS27.57-40-6' 'rv:11.0' 'rv:33.0' 'rv:35.0' 'rv:38.0' 'rv:41.0' 'rv:43.0' 'rv:44.0' 'rv:45.0' 'rv:46.0' 'rv:47.0' 'rv:48.0' 'rv:50.0' 'rv:51.0' 'rv:52.0' 'rv:55.0' 'rv:56.0' 'rv:57.0' 'rv:58.0' 'rv:59.0' 'rv:60.0' 'rv:61.0' 'verykoolS5005' 'verykoolS5524' 'verykoolS5530 Build/LMY47I' 'vivo' nan]
# Frequency encoding for DeviceInfo
self_encode_False=['DeviceInfo']
train, test = frequency_encoding(train, test, self_encode_False, self_encoding=False)
# Drop the original column
train.drop('DeviceInfo', axis=1, inplace=True)
test.drop('DeviceInfo', axis=1, inplace=True)
gc.collect()
8004
#pickling datasets
#Save 'train' data to a pickle file named 'train_1=3.pkl'
train.to_pickle(r'C:\Fraud_Data\data\train_3.pkl')
#save 'test' data to a pickle file named 'test_3.pkl'
test.to_pickle(r'C:\Fraud_Data\data\test_3.pkl')
import pandas as pd
# Read the 'train_3.pkl' pickle file and load it into the 'train' DataFrame
train = pd.read_pickle('./train_3.pkl')
# Read the 'test_3.pkl' pickle file and load it into the 'test' DataFrame
test = pd.read_pickle('./test_3.pkl')
# Date columns are not indicators
train.drop('TransactionDT', axis=1, inplace=True)
train.drop('DT', axis=1, inplace=True)
test.drop('TransactionDT', axis=1, inplace=True)
test.drop('DT', axis=1, inplace=True)
pd.DataFrame(train.columns).to_clipboard()
# We have 128 independent variables 1 dependent variable last.
len(train.columns)
128
info = train.dtypes
info.to_clipboard()
# Target variable for training set (y_train)
y_train = train['isFraud']
# Independent variables for training set (X_train)
X_train = train.drop(['isFraud'], axis=1)
# Target variable for test set (y_test)
y_test = test['isFraud']
# Independent variables for test set (X_test)
X_test = test.drop(['isFraud'], axis=1)
# Get the count of negative and positive examples
count_negative = (y_train == 0).sum()
count_positive = (y_train == 1).sum()
# Calculate the value of scale_pos_weight
scale_pos_weight = math.sqrt(count_negative / count_positive)
#XGBoost with default parameters
xgb = XGBClassifier(objective='binary:logistic', random_state=1003, eval_metric='auc', scale_pos_weight=scale_pos_weight)
xgb.fit(X_train, y_train)
#prediction
y_pred_xgb = xgb.predict(X_test)
#classification report
print(classification_report(y_test, y_pred_xgb))
#confusion matrix
print(confusion_matrix(y_test, y_pred_xgb, normalize='true'))
precision recall f1-score support
0 0.98 0.99 0.98 142535
1 0.50 0.42 0.46 5100
accuracy 0.97 147635
macro avg 0.74 0.70 0.72 147635
weighted avg 0.96 0.97 0.96 147635
[[0.98502824 0.01497176]
[0.57686275 0.42313725]]
# Probas for train
y_train_xgb_proba = xgb.predict_proba(X_train)[:, 1]
train_auc = roc_auc_score(y_train, y_train_xgb_proba)
print(f'Train AUC: {train_auc}')
# Probas for test
y_pred_xgb_proba = xgb.predict_proba(X_test)[:, 1]
test_auc = roc_auc_score(y_test, y_pred_xgb_proba)
print(f'Test AUC: {test_auc}')
Train AUC: 0.9625262580415187 Test AUC: 0.8849532849516837
# Feature Importance
cols = list( X_train.columns)
feature_imp = pd.DataFrame(sorted(zip(xgb.feature_importances_, cols), key=lambda x: x[0], reverse=True), columns=['Value', 'Feature'])
feature_imp.to_clipboard()
# Plotting feature importance with all variables (127 vars)
plt.figure(figsize=(20, 10))
sns.barplot(x="Value", y="Feature", data=feature_imp.sort_values(by="Value", ascending=False))
plt.title('XGB Most Important Features')
plt.tight_layout()
plt.show()
# Select the top 50 important features from X_train
selected_features = feature_imp.head(50)['Feature'].tolist()
# Creating new x dataframes
X_train_2 = X_train[selected_features]
X_test_2 = X_test[selected_features]
# Run the default model with the new feature set
#XGBoost with default parameters
xgb_2 = XGBClassifier(objective='binary:logistic', random_state=1003, eval_metric='auc', scale_pos_weight=scale_pos_weight)
xgb_2.fit(X_train_2, y_train) # fitting with X_train_2
#prediction
y_pred_xgb_2 = xgb_2.predict(X_test_2)
#classification report
print(classification_report(y_test, y_pred_xgb_2))
#confusion matrix
print(confusion_matrix(y_test, y_pred_xgb_2, normalize='true'))
precision recall f1-score support
0 0.98 0.98 0.98 142535
1 0.47 0.41 0.44 5100
accuracy 0.96 147635
macro avg 0.73 0.70 0.71 147635
weighted avg 0.96 0.96 0.96 147635
[[0.98364612 0.01635388]
[0.58705882 0.41294118]]
# Probas for train
y_train_xgb_proba_2 = xgb_2.predict_proba(X_train_2)[:, 1]
train_auc = roc_auc_score(y_train, y_train_xgb_proba_2)
print(f'Train AUC: {train_auc}')
# Probas for test
y_pred_xgb_proba_2 = xgb_2.predict_proba(X_test_2)[:, 1]
test_auc = roc_auc_score(y_test, y_pred_xgb_proba_2)
print(f'Test AUC: {test_auc}')
Train AUC: 0.9514796162751223 Test AUC: 0.8728808582962423
# Feature Importance
cols = list( X_train_2.columns)
feature_imp = pd.DataFrame(sorted(zip(xgb_2.feature_importances_, cols), key=lambda x: x[0], reverse=True), columns=['Value', 'Feature'])
feature_imp.to_clipboard()
# Select the top 30 important features from X_train
selected_features = feature_imp.head(40)['Feature'].tolist()
# Creating new x dataframes
X_train_3 = X_train[selected_features]
X_test_3 = X_test[selected_features]
# Run the default model with the new feature set
#XGBoost with default parameters
xgb_3 = XGBClassifier(objective='binary:logistic', random_state=1003, eval_metric='auc', scale_pos_weight=scale_pos_weight)
xgb_3.fit(X_train_3, y_train) # fitting with X_train_2
#prediction
y_pred_xgb_3 = xgb_3.predict(X_test_3)
#classification report
print(classification_report(y_test, y_pred_xgb_3))
#confusion matrix
print(confusion_matrix(y_test, y_pred_xgb_3, normalize='true'))
precision recall f1-score support
0 0.98 0.98 0.98 142535
1 0.45 0.42 0.44 5100
accuracy 0.96 147635
macro avg 0.72 0.70 0.71 147635
weighted avg 0.96 0.96 0.96 147635
[[0.98182201 0.01817799]
[0.58019608 0.41980392]]
# Probas for train
y_train_xgb_proba_3 = xgb_3.predict_proba(X_train_3)[:, 1]
train_auc = roc_auc_score(y_train, y_train_xgb_proba_3)
print(f'Train AUC: {train_auc}')
# Probas for test
y_pred_xgb_proba_3 = xgb_3.predict_proba(X_test_3)[:, 1]
test_auc = roc_auc_score(y_test, y_pred_xgb_proba_3)
print(f'Test AUC: {test_auc}')
Train AUC: 0.9501872964334457 Test AUC: 0.8720227291955123
# Feature Importance
cols = list( X_train_3.columns)
feature_imp = pd.DataFrame(sorted(zip(xgb_3.feature_importances_, cols), key=lambda x: x[0], reverse=True), columns=['Value', 'Feature'])
feature_imp.to_clipboard()
# Plotting feature importance with selected 40 params
plt.figure(figsize=(20, 10))
sns.barplot(x="Value", y="Feature", data=feature_imp.sort_values(by="Value", ascending=False))
plt.title('XGB Most Important Features')
plt.tight_layout()
plt.show()